Diagnostic for lung disorders using class prediction

ABSTRACT

The present invention provides methods for diagnosis and prognosis of lung cancer using expression analysis of one or more groups of genes, and a combination of expression analysis with bronchoscopy. The methods of the invention provide far superior detection accuracy for lung cancer when compared to any other currently available method for lung cancer diagnostic or prognosis. The invention also provides methods of diagnosis and prognosis of other lung diseases, particularly in individuals who are exposed to air pollutants, such as cigarette or cigar smoke, smog, asbestos and the like air contaminants or pollutants.

CROSS-REFERENCE TO RELATED APPLICATIONS Related Applications

The present application is a continuation of U.S. application Ser. No. 13/524,749, filed on Jun. 15, 2012, which is a continuation of U.S. application Ser. No. 12/869,525, filed on Aug. 26, 2010, which is a continuation of U.S. application Ser. No. 11/918,588, filed Feb. 8, 2008, which is a national stage filing under 35 U.S.C. 371 of International Application PCT/US2006/014132, filed Apr. 14, 2006, which claims the benefit of priority under 35 U.S.C. 119(e) to U.S. provisional application Ser. No. 60/671,243, filed on Apr. 14, 2005, the contents of which is are herein incorporated by reference in their entirety. International Application PCT/US2006/014132 was published under PCT Article 21(2) in English.

GOVERNMENT FUNDING

This invention was made with Government Support under Contract No. HL 071771 awarded by the National Institutes of Health. The Government has certain rights in the invention.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention is directed to diagnostic and prognostic methods by using analysis of gene group expression patterns in a subject. More specifically, the invention is directed to diagnostic and prognostic methods for detecting lung diseases, particularly lung cancer in subjects, preferably humans that have been exposed to air pollutants.

Background

Lung disorders represent a serious health problem in the modern society. For example, lung cancer claims more than 150,000 lives every year in the United States, exceeding the combined mortality from breast, prostate and colorectal cancers. Cigarette smoking is the most predominant cause of lung cancer. Presently, 25% of the U.S. population smokes, but only 10% to 15% of heavy smokers develop lung cancer. There are also other disorders associated with smoking such as emphysema. There are also health questions arising from people exposed to smokers, for example, second hand smoke. Former smokers remain at risk for developing such disorders including cancer and now constitute a large reservoir of new lung cancer cases. In addition to cigarette smoke, exposure to other air pollutants such as asbestos, and smog, pose a serious lung disease risk to individuals who have been exposed to such pollutants.

Approximately 85% of all subjects with lung cancer die within three years of diagnosis. Unfortunately survival rates have not changed substantially of the past several decades. This is largely because there are no effective methods for identifying smokers who are at highest risk for developing lung cancer and no effective tools for early diagnosis.

The methods that are currently employed to diagnose lung cancer include chest X-ray analysis, bronchoscopy or sputum cytological analysis, computer tomographic analysis of the chest, and positron electron tomographic (PET) analysis. However, none of these methods provide a combination of both sensitivity and specificity needed for an optimal diagnostic test.

Classification of human lung cancer by gene expression profiling has been described in several recent publications (M. Garber, “Diversity of gene expression in adenocarcinoma of the lung,” PNAS, 98(24): 13784-13789 (2000); A. Bhattacharjee, “Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses,” PNAS, 98(24):13790-13795 (2001)), but no specific gene set is used as a classifier to diagnose lung cancer in bronchial epithelial tissue samples.

Moreover, while it appears that a subset of smokers are more susceptible to, for example, the carcinogenic effects of cigarette smoke and are more likely to develop lung cancer, the particular risk factors, and particularly genetic risk factors, for individuals have gone largely unidentified. Same applies to lung cancer associated with, for example, asbestos exposure.

Therefore, there exists a great need to develop sensitive diagnostic methods that can be used for early diagnosis and prognosis of lung diseases, particularly in individuals who are at risk of developing lung diseases, particularly individuals who are exposed to air pollutants such as cigarette/cigar smoke, asbestos and other toxic air pollutants.

SUMMARY OF THE INVENTION

The present invention provides compositions and methods for diagnosis and prognosis of lung diseases which provides a diagnostic test that is both very sensitive and specific.

We have found a group of gene transcripts that we can use individually and in groups or subsets for enhanced diagnosis for lung diseases, such as lung cancer, using gene expression analysis. We provide detailed guidance on the increase and/or decrease of expression of these genes for diagnosis and prognosis of lung diseases, such as lung cancer.

One example of the gene transcript groups useful in the diagnostic/prognostic tests of the invention are set forth in Table 6. We have found that taking groups of at least 20 of the Table 6 genes provides a much greater diagnostic capability than chance alone.

Preferably one would use more than 20 of these gene transcript, for example about 20-100 and any combination between, for example, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, and so on. Our preferred groups are the groups of 96 (Table 1), 84 (Table 2), 50 (Table 3), 36 (Table 4), 80 (Table 5), 535 (Table 6) and 20 (Table 7). In some instances, we have found that one can enhance the accuracy of the diagnosis by adding certain additional genes to any of these specific groups. When one uses these groups, the genes in the group are compared to a control or a control group. The control groups can be non-smokers, smokers, or former smokers. Preferably, one compares the gene transcripts or their expression product in the biological sample of an individual against a similar group, except that the members of the control groups do not have the lung disorder, such as emphysema or lung cancer. For example, comparing can be performed in the biological sample from a smoker against a control group of smokers who do not have lung cancer. When one compares the transcripts or expression products against the control for increased expression or decreased expression, which depends upon the particular gene and is set forth in the tables—not all the genes surveyed will show an increase or decrease. However, at least 50% of the genes surveyed must provide the described pattern. Greater reliability if obtained as the percent approaches 100%. Thus, in one embodiment, one wants at least 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 98%, or 99% of the genes surveyed to show the altered pattern indicative of lung disease, such as lung cancer, as set forth in the tables, infra.

In one embodiment, the invention provides a group of genes the expression of which is altered in individuals who are at risk of developing lung diseases, such as lung cancer, because of the exposure to air pollutants. The invention also provides groups of genes the expression of which is consistently altered as a group in individuals who are at risk of developing lung diseases because of the exposure to air pollutants.

The present invention provides gene groups the expression pattern or profile of which can be used in methods to diagnose lung diseases, such as lung cancer and even the type of lung cancer, in more than 60%, preferably more than 65%, still more preferably at least about 70%, still more preferably about 75%, or still more preferably about 80%-95% accuracy from a sample taken from airways of an individual screened for a lung disease, such as lung cancer.

In one embodiment, the invention provides a method of diagnosing a lung disease such as lung cancer using a combination of bronchoscopy and the analysis of gene expression pattern of the gene groups as described in the present invention.

Accordingly, the invention provides gene groups that can be used in diagnosis and prognosis of lung diseases. Particularly, the invention provides groups of genes the expression profile of which provides a diagnostic and or prognostic test to determine lung disease in an individual exposed to air pollutants. For example, the invention provides groups of genes the expression profile of which can distinguish individuals with lung cancer from individuals without lung cancer.

In one embodiment, the invention provides an early asymptomatic screening system for lung cancer by using the analysis of the disclosed gene expression profiles. Such screening can be performed, for example, in similar age groups as colonoscopy for screening colon cancer. Because early detection in lung cancer is crucial for efficient treatment, the gene expression analysis system of the present invention provides a vastly improved method to detect tumor cells that cannot yet be discovered by any other means currently available.

The probes that can be used to measure expression of the gene groups of the invention can be nucleic acid probes capable of hybridizing to the individual gene/transcript sequences identified in the present invention, or antibodies targeting the proteins encoded by the individual gene group gene products of the invention. The probes are preferably immobilized on a surface, such as a gene or protein chip so as to allow diagnosis and prognosis of lung diseases in an individual.

In one embodiment, the invention provides a group of genes that can be used as individual predictors of lung disease. These genes were identified using probabilities with a t-test analysis and show differential expression in smokers as opposed to non-smokers. The group of genes comprise ranging from 1 to 96, and all combinations in between, for example 5, 10, 15, 20, 25, 30, for example at least 36, at least about, 40, 45, 50, 60, 70, 80, 90, or 96 gene transcripts, selected from the group consisting of genes identified by the following GenBank sequence identification numbers (the identification numbers for each gene are separated by “;” while the alternative GenBank ID numbers are separated by “///”): NM_003335; NM_000918; NM_006430.1; NM_001416.1; NM_004090; NM_006406.1; NM_003001.2; NM_001319; NM_006545.1; NM_021145.1; NM_002437.1; NM_006286; NM_001003698 /// NM_001003699 /// NM_002955; NM_001123 /// NM_006721; NM_024824; NM_004935.1; NM_002853.1; NM_019067.1; NM_024917.1; NM_020979.1; NM_005597.1; NM_007031.1; NM_009590.1; NM_020217.1; NM_025026.1; NM_014709.1; NM_014896.1; AF010144; NM_005374.1; NM_001696; NM_005494 /// NM_058246; NM_006534 /// NM_181659; NM_006368; NM_002268 /// NM_032771; NM_014033; NM_016138; NM_007048 /// NM_194441; NM_006694; NM_000051 /// NM_138292 /// NM_138293; NM_000410 /// NM_139002 /// NM_139003 /// NM_139004 /// NM_139005 /// NM_139006 /// NM_139007 /// NM_139008 /// NM_139009 /// NM_139010 /// NM_139011; NM_004691; NM_012070 /// NM_139321 /// NM_139322; NM_006095; AI632181; AW024467; NM_021814; NM_005547.1; NM_203458; NM_015547 /// NM_147161; AB007958.1; NM_207488; NM_005809 /// NM_181737 /// NM_181738; NM_016248 /// NM_144490; AK022213.1; NM_005708; NM_207102; AK023895; NM_144606 /// NM_144997; NM_018530; AK021474; U43604.1; AU147017; AF222691.1; NM_015116; NM_001005375 /// NM_001005785 /// NM_001005786 /// NM_004081 /// NM_020363 /// NM_020364 /// NM_020420; AC004692; NM_001014; NM_000585 /// NM_172174 /// NM_172175; NM_054020 /// NM_172095 /// NM_172096 /// NM_172097; BE466926; NM_018011; NM_024077; NM_012394; NM_019011 /// NM_207111 /// NM_207116; NM_017646; NM_021800; NM_016049; NM_014395; NM_014336; NM_018097; NM_019014; NM_024804; NM_018260; NM_018118; NM_014128; NM_024084; NM_005294; AF077053; NM_138387; NM_024531; NM_000693; NM_018509; NM_033128; NM_020706; AI523613; and NM_014884, the expression profile of which can be used to diagnose lung disease, for example lung cancer, in lung cell sample from a smoker, when the expression pattern is compared to the expression pattern of the same group of genes in a smoker who does not have or is not at risk of developing lung cancer.

In another embodiment, the gene/transcript analysis comprises a group of about 10-20, 20-30, 30-40, 40-50, 50-60, 60-70, 70-80, 80, 80-90, 90-100, 100-120, 120-140, 140-150, 150-160, 160-170, 170-180, 180-190, 190-200, 200-210, 210-220, 220-230, 230-240, 240-250, 250-260, 260-270, 270-280, 280-290, 290-300, 300-310, 310-320, 320-330, 330-340, 340-350, 350-360, 360-370, 370-380, 380-390, 390-400, 400-410, 410-420, 420-430, 430-440, 440-450, 450-460, 460-470, 470-480, 480-490, 490-500, 500-510, 510-520, 520-530, and up to about 535 genes selected from the group consisting of genes or transcripts as shown in the Table 6.

In one embodiment, the genes are selected from the group consisting of genes or transcripts as shown in Table 5.

In another embodiment, the genes are selected from the genes or transcripts as shown in Table 7.

In one embodiment, the transcript analysis gene group comprises a group of individual genes the change of expression of which is predictive of a lung disease either alone or as a group, the gene transcripts selected from the group consisting of NM_007062.1; NM_001281.1; BC002642.1; NM_000346.1; NM_006545.1; BG034328; NM_019067.1; NM_017925.1; NM_017932.1; NM_030757.1; NM_030972.1; NM_002268 /// NM_032771; NM_007048 /// NM_194441; NM_006694; U85430.1; NM_004691; AB014576.1; BF218804; BE467941; R83000; AL161952.1; AK023843.1; AK021571.1; AK023783.1; AL080112.1; AW971983; AI683552; NM_024006.1; AK026565.1; NM_014182.1; NM_021800.1; NM_016049.1; NM_021971.1; NM_014128.1; AA133341; AF198444.1.

In one embodiment, the gene group comprises a probe set capable of specifically hybridizing to at least all of the 36 gene products. Gene product can be mRNA which can be recognized by an oligonucleotide or modified oligonucleotide probe, or protein, in which case the probe can be, for example an antibody specific to that protein or an antigenic epitope of the protein.

In yet another embodiment, the invention provides a gene group, wherein the expression pattern of the group of genes provides diagnostic for a lung disease. The gene group comprises gene transcripts encoded by a gene group consisting of at least for example 5, 10, 15, 20, 25, 30, preferably at least 36, still more preferably 40, still more preferably 45, and still more preferably 46, 47, 48, 49, or all 50 of the genes selected from the group consisting of and identified by their GenBank identification numbers: NM_007062.1; NM_001281.1; BC000120.1; NM_014255.1; BC002642.1; NM_000346.1; NM_006545.1; BG034328; NM_021822.1; NM_021069.1; NM_019067.1; NM_017925.1; NM_017932.1; NM_030757.1; NM_030972.1; AF126181.1; U 93240.1; U90552.1; AF151056.1; U85430.1; U51007.1; BC005969.1; NM_002271.1; AL566172; AB014576.1; BF218804; AK022494.1; AA114843; BE467941; NM_003541.1; R83000; AL161952.1; AK023843.1; AK021571.1; AK023783.1; AU147182; AL080112.1; AW971983; AI683552; NM_024006.1; AK026565.1; NM_014182.1; NM_021800.1; NM_016049.1; NM_019023.1; NM_021971.1; NM_014128.1; AK025651.1; AA133341; and AF198444.1. In one preferred embodiment, one can use at least 20 of the 36 genes that overlap with the individual predictors and, for example, 5-9 of the non-overlapping genes and combinations thereof.

In another embodiment, the invention provides a group of about 30-180, preferably, a group of about 36-150 genes, still more preferably a group of about 36-100, and still more preferably a group of about 36-50 genes, the expression profile of which is diagnostic of lung cancer in individuals who smoke.

In one embodiment, the invention provides a group of genes the expression of which is decreased in an individual having lung cancer. In one embodiment, the group of genes comprises at least 5-10, 10-15, 15-20, 20-25 genes selected from the group consisting of NM_000918; NM_006430.1; NM_001416.1; MM_004090; NM_006406.1; NM_003001.2; NM_006545.1; NM_002437.1; NM_006286; NM_001123 /// NM_006721; NM_024824; NM_004935.1; NM_001696; NM_005494 /// NM_058246; NM_006368; NM_002268 /// NM_032771; NM_006694; NM_004691; NM_012394; NM_021800; NM_016049; NM_138387; NM_024531; and NM_018509. One or more other genes can be added to the analysis mixtures in addition to these genes.

In another embodiment, the group of genes comprises genes selected from the group consisting of NM_014182.1; NM_001281.1; NM_024006.1; AF135421.1; L76200.1; NM_000346.1; BC008710.1; BC000423.2; BC008710.1; NM_007062; BC075839.1 ///BC073760.1; BG072436.1 /// BC004560.2; BC001016.2; BC005023.1; BC000360.2; BC007455.2; BC023528.2 /// BC047680.1; BC064957.1; BC008710.1; BC066329.1; BC023976.2; BC008591.2 /// BC050440. /// BC048096.1; and BC028912.1.

In yet another embodiment, the group of genes comprises genes selected from the group consisting of NM_007062.1; NM_001281.1; BC000120.1; NM_014255.1; BC002642.1; NM_000346.1; NM_006545.1; BG034328; NM_021822.1; NM_021069.1; NM_019067.1; NM_017925.1; NM_017932.1; NM_030757.1; NM_030972.1; AF126181.1; U93240.1; U90552.1; AF151056.1; U85430.1; U51007.1; BC005969.1; NM_002271.1; AL566172; and AB014576.1.

In one embodiment, the invention provides a group of genes the expression of which is increased in an individual having lung cancer. In one embodiment, the group of genes comprises genes selected from the group consisting of NM_003335; NM_001319; NM_21145.1; NM_001003698 /// NM_001003699 ///; NM_002955; NM_002853.1; NM_019067.1; NM_024917.1; NM_020979.1; NM_005597.1; NM_007031.1; NM_009590.1; NM_020217.1; NM_025026.1; NM_014709.1; NM_014896.1; AF010144; NM_005374.1; NM_006534 /// NM_181659; NM_014033; NM_016138; NM_007048 /// NM_194441; NM_000051 /// NM_138292 /// NM_138293; NM_000410 /// NM_139002 /// NM_139003 /// NM_139004 /// NM_139005 /// NM_139006 /// NM_139007 /// NM_139008 /// NM_139009 /// NM_139010 /// NM_139011; NM_012070 /// NM_139321 /// NM_139322; NM_006095; A1632181; AW024467; NM_021814; NM_005547.1; NM_203458; NM_015547 /// NM_147161, AB007958.1; NM_207488; NM_005809 /// NM_181737 /// NM_181738; NM_016248 /// NM_144490; AK022213.1; NM_005708; NM_207102; AK023895; NM_144606 /// NM_144997; NM_018530; AK021474; U43604.1; AU147017; AF222691.1; NM_015116; NM_001005375 /// NM 001005785 /// NM_001005786 /// NM_004081 /// NM_020363 /// NM_020364 /// NM_020420; AC004692; NM_001014; NM_000585 /// NM_172174 /// NM_172175; NM_054020 /// NM_172095 /// NM_172096 /// NM_172097; BE466926; NM_018011; NM_024077; NM_019011 /// NM_207111 /// NM_207116; NM_017646; NM_014395; NM_014336; NM_018097; NM_019014; NM_024804; NM_018260; NM_018118; NM_014128; NM_024084; NM_005294; AF077053; NM_000693; NM_033128; NM_020706; AI523613; and NM_014884.

In one embodiment, the group of genes comprises genes selected from the group consisting of NM_030757.1; R83000; AK021571.1; NM_17932.1; U85430.1; A1683552; BC002642.1; AW024467; NM_030972.1; BC021135.1; AL161952.1; AK026565.1; AK023783.1; BF218804; AK023843.1; BC001602.1; BC034707.1; BC064619.1; AY280502.1; BC059387.1; BC061522.1; U50532.1; BC006547.2; BC008797.2; BC000807.1; AL080112.1; BC033718.1 /// BC046176.1 ///; BC038443.1; Hs.288575 (UNIGENE ID); AF020591.1; BC002503.2; BC009185.2; Hs.528304 (UNIGENE ID); U50532.1; BC013923.2; BC031091; Hs.249591 (Unigene ID); Hs.286261 (Unigene ID); AF348514.1; BC066337.1 /// BC058736.1 ///BC050555.1; Hs.216623 (Unigene ID); BC072400.1; BC041073.1; U43965.1; BC021258.2; BC016057.1; BC016713.1 /// BC014535.1 /// AF237771.1; BC000701.2; BC010067.2; Hs.156701 (Unigene ID); BC030619.2; U43965.1; Hs.438867 (Unigene ID); BC035025.2 ///BC050330.1; BC074852.2 /// BC074851.2; Hs.445885 (Unigene ID); AF365931.1; and AF257099.1.

In one embodiment, the group of genes comprises genes selected from the group consisting of BF218804; AK022494.1; AA114843; BE467941; NM 003541.1; R83000; AL161952.1; AK023843.1; AK021571.1; AK023783.1; AU147182; AL080112.1; AW971983; AI683552; NM_024006.1; AK026565.1; NM_014182.1; NM_021800.1; NM_016049.1; NM_019023.1; NM_021971.1; NM_014128.1; AK025651.1; AA133341; and AF198444.1.

In another embodiment, the invention provides a method for diagnosing a lung disease comprising obtaining a nucleic acid sample from lung, airways or mouth of an individual exposed to an air pollutant, analyzing the gene transcript levels of one or more gene groups provided by the present invention in the sample, and comparing the expression pattern of the gene group in the sample to an expression pattern of the same gene group in an individual, who is exposed to similar air pollutant but not having lung disease, such as lung cancer or emphysema, wherein the difference in the expression pattern is indicative of the test individual having or being at high risk of developing a lung disease. The decreased expression of one or more of the genes, preferably all of the genes including the genes listed on Tables 1-4 as “down” when compared to a control, and/or increased expression of one or more genes, preferably all of the genes listed on Tables 1-4 as “up” when compared to an individual exposed to similar air pollutants who does not have a lung disease, is indicative of the person having a lung disease or being at high risk of developing a lung disease, preferably lung cancer, in the near future and needing frequent follow ups to allow early treatment of the disease.

In one preferred embodiment, the lung disease is lung cancer. In one embodiment, the air pollutant is cigarette smoke.

Alternatively, the diagnosis can separate the individuals, such as smokers, who are at lesser risk of developing lung diseases, such as lung cancer by analyzing the expression pattern of the gene groups of the invention provides a method of excluding individuals from invasive and frequent follow ups.

Accordingly, the invention provides methods for prognosis, diagnosis and therapy designs for lung diseases comprising obtaining an airway sample from an individual who smokes and analyzing expression profile of the gene groups of the present invention, wherein an expression pattern of the gene group that deviates from that in a healthy age, race, and gender matched smoker, is indicative of an increased risk of developing a lung disease. Tables 1-4 indicate the expression pattern differences as either being down or up as compared to a control, which is an individual exposed to similar airway pollutant but not affected with a lung disease.

The invention also provides methods for prognosis, diagnosis and therapy designs for lung diseases comprising obtaining an airway sample from a non-smoker individual and analyzing expression profile of the gene groups of the present invention, wherein an expression pattern of the gene group that deviates from that in a healthy age, race, and gender matched smoker, is indicative of an increased risk of developing a lung disease.

In one embodiment, the analysis is performed from a biological sample obtained from bronchial airways.

In one embodiment, the analysis is performed from a biological sample obtained from buccal mucosa.

In one embodiment, the analysis is performed using nucleic acids, preferably RNA, in the biological sample.

In one embodiment, the analysis is performed analyzing the amount of proteins encoded by the genes of the gene groups of the invention present in the sample.

In one embodiment the analysis is performed using DNA by analyzing the gene expression regulatory regions of the groups of genes of the present invention using nucleic acid polymorphisms, such as single nucleic acid polymorphisms or SNPs, wherein polymorphisms known to be associated with increased or decreased expression are used to indicate increased or decreased gene expression in the individual. For example, methylation patterns of the regulatory regions of these genes can be analyzed.

In one embodiment, the present invention provides a minimally invasive sample procurement method for obtaining airway epithelial cell RNA that can be analyzed by expression profiling of the groups of genes, for example, by array-based gene expression profiling. These methods can be used to diagnose individuals who are already affected with a lung disease, such as lung cancer, or who are at high risk of developing lung disease, such as lung cancer, as a consequence of being exposed to air pollutants. These methods can also be used to identify further patterns of gene expression that are diagnostic of lung disorders/diseases, for example, cancer or emphysema, and to identify subjects at risk for developing lung disorders.

The invention further provides a gene group microarray consisting of one or more of the gene groups provided by the invention, specifically intended for the diagnosis or prediction of lung disorders or determining susceptibility of an individual to lung disorders.

In one embodiment, the invention relates to a method of diagnosing a disease or disorder of the lung comprising obtaining a sample, nucleic acid or protein sample, from an individual to be diagnosed; and determining the expression of group of identified genes in said sample, wherein changed expression of such gene compared to the expression pattern of the same gene in a healthy individual with similar life style and environment is indicative of the individual having a disease of the lung.

In one embodiment, the invention relates to a method of diagnosing a disease or disorder of the lung comprising obtaining at least two samples, nucleic acid or protein samples, in at least one time interval from an individual to be diagnosed; and determining the expression of the group of identified genes in said sample, wherein changed expression of at least about for example 5, 10, 15, 20, 25, 30, preferably at least about 36, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, or 180 of such genes in the sample taken later in time compared to the sample taken earlier in time is diagnostic of a lung disease.

In one embodiment, the disease of the lung is selected from the group consisting of asthma, chronic bronchitis, emphysema, primary pulmonary hypertension, acute respiratory distress syndrome, hypersensitivity pneumonitis, eosinophilic pneumonia, persistent fungal infection, pulmonary fibrosis, systemic sclerosis, idiopathic pulmonary hemosiderosis, pulmonary alveolar proteinosis, and lung cancer, such as adenocarcinoma, squamous cell carcinoma, small cell carcinoma, large cell carcinoma, and benign neoplasm of the lung (e.g., bronchial adenomas and hamartomas).

In a particular embodiment, the nucleic acid sample is RNA.

In a preferred embodiment, the nucleic acid sample is obtained from an airway epithelial cell. In one embodiment, the airway epithelial cell is obtained from a bronchoscopy or buccal mucosal scraping.

In one embodiment, individual to be diagnosed is an individual who has been exposed to tobacco smoke, an individual who has smoked, or an individual who currently smokes.

The invention also provides an array, for example, a microarray for diagnosis of a disease of the lung having immobilized thereon a plurality of oligonucleotides which hybridize specifically to genes of the gene groups which are differentially expressed in airways exposed to air pollutants, such as cigarette smoke, and have or are at high risk of developing lung disease, as compared to those individuals who are exposed to similar air pollutants and airways which are not exposed to such pollutants. In one embodiment, the oligonucleotides hybridize specifically to one allelic form of one or more genes which are differentially expressed for a disease of the lung. In a particular embodiment, the differentially expressed genes are selected from the group consisting of the genes shown in tables 1-4; preferably the group of genes comprises genes selected from the Table 3. In one preferred embodiment, the group of genes comprises the group of at least 20 genes selected from Table 3 and additional 5-10 genes selected from Tables 1 and 2. In one preferred embodiment, at least about 10 genes are selected from Table 4.

BRIEF DESCRIPTION OF FIGURES

FIG. 1 shows Table 1, which sets forth a listing a group of 96 genes, their expression profile in lung cancer as compared to an individual not having lung cancer but being exposed to similar environmental stress, i.e. air pollutant, in this example, cigarette smoke. These genes were identified using Student's t-test.

FIG. 2 shows Table 2, listing a group of 84 genes, their expression profile in lung cancer as compared to an individual not having lung cancer but being exposed to similar environmental stress, i.e. air pollutant, in this example, cigarette smoke. These genes were identified using Student's t-test.

FIG. 3 shows Table 3, listing a group of 50 genes, and their expression profile in lung cancer as compared using a class-prediction model to an individual not having lung cancer but being exposed to similar environmental stress, i.e. air pollutant, in this example, cigarette smoke.

FIG. 4 shows Table 4, listing a group of 36 genes, their expression profile in lung cancer as compared to an individual not having lung cancer but being exposed to similar environmental stress, i.e. air pollutant, in this example, cigarette smoke. This group of genes is a combination of predictive genes identified using both Student's t-test and class-prediction model.

FIG. 5 shows an example of the results using class prediction model as obtained in Example 1. Training set included 74 samples, and the test set 24 samples. The mean age for the training set was 55 years, and the mean pack years smoked by the training set was 38. The mean age for the test set was 56 years, and the mean pack years smoked by the test set was 41.

FIG. 6 shows an example of the 50 gene class prediction model obtained in Example 1. Each square represents expression of one transcript. The transcript can be identified by the probe identifier on the y-axis according to the Affymetrix Human Genome Gene chip U133 probe numbers (see Appendix). The individual samples are identified on the x-axis. The samples are shown in this figure as individuals with lung cancer (“cancer”) and individuals without lung cancer (“no cancer”). The gene expression is shown as higher in darker squares and lower in lighter squares. One can clearly see the differences between the gene expression of these 50 genes in these two groups just by visually observing the pattern of lighter and darker squares.

FIG. 7 shows a comparison of sample-quality metrics. The graph plots the Affymetrix MAS 5.0 percent present (y-axis) versus the z-score derived filter (x-axis). The two metrics have a correlation (R2) of 0.82.

FIG. 8 shows distribution of accuracies for real vs. random 1000 runs. Histogram comparing test set class prediction accuracies of 1000 “sample randomized” classifiers generated by randomly assigning samples into training and test sets with true class labels (unshaded) versus 1000 “sample and class randomized” classifiers where the training set class labels were randomized following sample assignment to the training or test set (shaded).

FIG. 9 shows classification accuracy as a function of the average prediction strength over the 1000 runs of the algorithm with different training/test sets.

FIG. 10A shows the number of times each of the 80-predictive probe sets from the actual biomarker was present in the predictive lists of 80 probe sets derived from 1000 runs of the algorithm.

FIG. 10B shows the Number of times a probe set was present in the predictive lists of 80 probe sets derived from 1000 random runs of the algorithm described in Supplemental Table 7.

FIG. 11 shows Boxplot of the Prediction Strength values of the test set sample predictions made by the Weighted Voting algorithm across the 1000 runs with different training and test sets. The black boxplots (first two boxes from the left) are derived from the actual training and test set data with correct sample labels, the grey boxplots (last two boxes on the right) are derived from the test set predictions based on training sets with randomized sample labels.

FIG. 12 shows homogeneity of gene expression in large airway samples from smokers with lung cancer of varying cell types. Principal Component Analysis (PCA) was performed on the gene-expression measurements for the 80 genes in our predictor and all of the airway epithelium samples from patients with lung cancer. Gene expression measurements were Z(0,1) normalized prior to PCA. The graph shows the sample loadings for the first two principal components which together account for 58% of the variation among samples from smokers with cancer. There is no apparent separation of the samples with regard to lung tumor subtype.

FIG. 13 shows real time RT-PCR and microarray data for selected genes distinguishing smokers with and without cancer. Fold change for each gene is shown as the ratio of average expression level of cancer group (n=3) to the average expression of non-cancer group (n=3). Four genes (IL8, FOS, TPD52, and RAB1A) were found to be up-regulated in cancer group on both microarray and RT-PCR platforms; three genes (DCLRE1C, BACH2, and DUOX1) were found to be down-regulated in cancer group on both platforms.

FIG. 14 shows the class prediction methodology used. 129 samples (69 from patients without cancer; 60 from patients with lung cancer) were separated into a training (n=77) and a test set (n=52). The most frequently chosen 40 up- and 40 down-regulated genes from internal cross validation on the training set were selected for the final gene committee. The weighted voted algorithm using this committee of 80 genes was then used to predict the class of the test set samples.

FIG. 15 shows hierarchical clustering of class-predictor genes. Z-score-normalized gene-expression measurements of the eighty class-predictor genes in the 52 test-set samples are shown in a false-color scale and organized from top to bottom by hierarchical clustering. The Affymetrix U133A probeset ID and HUGO symbol are given to the right of each gene. The test-set samples are organized from left to right first by whether the patient had a clinical diagnosis of cancer. Within these two groups, the samples are organized by the accuracy of the class-predictor diagnosis (samples classified incorrectly are on the right shown in dark green). 43/52 (83%) test samples are classified correctly. The sample ID is given at the top of each column. The prediction strength of each of the diagnoses made by the class-prediction algorithm is indicated in a false-color scale immediately below the prediction accuracy. Prediction strength is a measure of the level of diagnostic confidence and varies on a continuous scale from 0 to 1 where 1 indicates a high degree of confidence.

FIG. 16 shows a Comparison of Receiver Operating Characteristic (ROC) curves. Sensitivity (y-axis) and 1-Specificity (x-axis) were calculated at various prediction strength thresholds where a prediction of no cancer was assigned a negative prediction strength value and a prediction of cancer was assigned a positive prediction strength value. The solid black line represents the ROC curve for the airway gene expression classifier. The dotted black line represents the average ROC curve for 1000 classifiers derived by randomizing the training set class labels (“class randomized”). The upper and lower lines of the gray shaded region represent the average ROC curves for the top and bottom half of random biomarkers (based on area under the curve). There is a significant difference between the area under the curve of the actual classifier and the random classifiers (p=0.004; empiric p-value based on permutation)

FIG. 17 shows the Principal Component Analysis (PCA) of biomarker gene expression in lung tissue samples. The 80 biomarker probesets were mapped to 64 probesets in the Bhattacharjee et al. HGU95Av2 microarray dataset of lung cancer and normal lung tissue. The PCA is a representation of the overall variation in expression of the 64 biomarker probesets. The normal lung samples (NL) are represented in green, the adenocarcinomas (AD) in red, the small cells (SC) in blue, and the squamous (SQ) lung cancer samples in yellow. The normal lung samples separate from the lung cancer samples along the first principal component (empirically derived p-value=0.023, see supplemental methods).

FIGS. 18A-18C show data obtained in this study. FIG. 18A shows bronchoscopy results for the 129 patients in the study. Only 32 of the 60 patients that had a final diagnosis of cancer had bronchoscopies that were diagnostic of lung cancer. The remaining 97 samples had bronchoscopies that were negative for lung cancer including 5 that had a definitive alternate benign diagnosis. This resulted in 92 patients with non-diagnostic bronchoscopy that required further tests and/or clinical follow-up. FIG. 18B shows biomarker prediction results. 36 of the 92 patients with non-diagnostic bronchoscopies exhibited a gene expression profile that was positive for lung cancer. This resulted in 25 of 28 cancer patients with non-diagnostic bronchoscopies being predicted to have cancer. FIG. 18C shows combined test results. In a combined test where a positive test result from either bronchoscopy or gene expression is considered indicative of lung cancer a sensitivity of 95% (57 of 60 cancer patients) with only a 16% false positive rate (11 of 69 non-cancer patients) is achieved. The shading of each contingency table is reflective of the overall fraction of each sample type in each quadrant.

FIGS. 19A-19B show a comparison of bronchoscopy and biomarker prediction by A) cancer stage or B) cancer subtype. Each square symbolizes one patient sample. The upper half represents the biomarker prediction accuracy and the lower half represents the bronchoscopy accuracy. Not all cancer samples are represented in this figure. FIG. 19A includes only Non Small Cell cancer samples that could be staged using the TMN system (48 of the 60 total cancer samples). FIG. 19B includes samples that could be histologically classified as Adenocarcinoma, Squamous Cell Carcinoma and Small Cell Carcinoma (45 of the 60 total cancer samples).

DETAILED DESCRIPTION OF THE INVENTION

The present invention is directed to gene/transcript groups and methods of using the expression profile of these gene/transcript groups in diagnosis and prognosis of lung diseases.

We provide a method that significantly increases the diagnostic accuracy of lung diseases, such as lung cancer. When one combines the gene expression analysis of the present invention with bronchoscopy, the diagnosis of lung cancer is dramatically better by detecting the cancer in an earlier stage than any other available method to date, and by providing far fewer false negatives and/or false positives than any other available method.

We have found a group of gene transcripts that we can use individually and in groups or subsets for enhanced diagnosis for lung diseases, such as lung cancer, using gene expression analysis. We provide detailed guidance on the increase and/or decrease of expression of these genes for diagnosis and prognosis of lung diseases, such as lung cancer.

One example of the gene transcript groups useful in the diagnostic/prognostic tests of the invention is set forth in Table 6. We have found that taking any group that has at least 20 of the Table 6 genes provides a much greater diagnostic capability than chance alone.

Preferably one would use more than 20 of these gene transcript, for example about 20-100 and any combination between, for example, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, and so on. Our preferred groups are the groups of 96 (Table 1), 84 (Table 2), 50 (Table 3), 36 (Table 4), 80 (Table 5), 535 (Table 6) and 20 (Table 7). In some instances, we have found that one can enhance the accuracy of the diagnosis by adding additional genes to any of these specific groups.

Naturally, following the teachings of the present invention, one may also include one or more of the genes and/or transcripts presented in Tables 1-7 into a kit or a system for a multicancer screening kit. For example, any one or more genes and or transcripts from Table 7 may be added as a lung cancer marker for a gene expression analysis.

When one uses these groups, the genes in the group are compared to a control or a control group. The control groups can be non-smokers, smokers, or former smokers. Preferably, one compares the gene transcripts or their expression product in the biological sample of an individual against a similar group, except that the members of the control groups do not have the lung disorder, such as emphysema or lung cancer. For example, comparing can be performed in the biological sample from a smoker against a control group of smokers who do not have lung cancer. When one compares the transcripts or expression products against the control for increased expression or decreased expression, which depends upon the particular gene and is set forth in the tables—not all the genes surveyed will show an increase or decrease. However, at least 50% of the genes surveyed must provide the described pattern. Greater reliability if obtained as the percent approaches 100%. Thus, in one embodiment, one wants at least 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 98%, 99% of the genes surveyed to show the altered pattern indicative of lung disease, such as lung cancer, as set forth in the tables as shown below.

The presently described gene expression profile can also be used to screen for individuals who are susceptible for lung cancer. For example, a smoker, who is over a certain age, for example over 40 years old, or a smoker who has smoked, for example, a certain number of years, may wish to be screened for lung cancer. The gene expression analysis as described herein can provide an accurate very early diagnosis for lung cancer. This is particularly useful in diagnosis of lung cancer, because the earlier the cancer is detected, the better the survival rate is.

For example, when we analyzed the gene expression results, we found, that if one applies a less stringent threshold, the group of 80 genes as presented in Table 5 are part of the most frequently chosen genes across 1000 statistical test runs (see Examples below for more details regarding the statistical testing). Using random data, we have shown that no random gene shows up more than 67 times out of 1000. Using such a cutoff, the 535 genes of Table 6 in our data show up more than 67 times out of 1000. All the 80 genes in Table 5 form a subset of the 535 genes. Table 7 shows the top 20 genes which are subset of the 535 list. The direction of change in expression is shown using signal to noise ratio. A negative number in Tables 5, 6, and 7 means that expression of this gene or transcript is up in lung cancer samples. Positive number in Table 5, 6, and 7, indicates that the expression of this gene or transcript is down in lung cancer.

Accordingly, any combination of the genes and/or transcripts of Table 6 can be used. In one embodiment, any combination of at least 5-10, 10-20, 20-30, 30-40, 40-50, 50-60, 60-70, 70-80, 80, 80-90, 90-100, 100-120, 120-140, 140-150, 150-160, 160-170, 170-180, 180-190, 190-200, 200-210, 210-220, 220-230, 230-240, 240-250, 250-260, 260-270, 270-280, 280-290, 290-300, 300-310, 310-320, 320-330, 330-340, 340-350, 350-360, 360-370, 370-380, 380-390, 390-400, 400-410, 410-420, 420-430, 430-440, 440-450, 450-460, 460-470, 470-480, 480-490, 490-500, 500-510, 510-520, 520-530, and up to about 535 genes selected from the group consisting of genes or transcripts as shown in the Table 6.

Table 7 provides 20 of the most frequently variably expressed genes in lung cancer when compared to samples without cancer. Accordingly, in one embodiment, any combination of about 3-5, 5-10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or all 20 genes and/or transcripts of Table 7, or any sub-combination thereof are used.

In one embodiment, the invention provides a gene group the expression profile of which is useful in diagnosing lung diseases and which comprises probes that hybridize ranging from 1 to 96 and all combinations in between for example 5, 10, 15, 20, 25, 30, 35, at least about 36, at least to 40, at least to 50, at least to 60, to at least 70, to at least 80, to at least 90, or all of the following 96 gene sequences: NM_003335; NM_000918; NM_006430.1; NM_001416.1; NM_004090; NM_006406.1; NM_003001.2; NM_001319; NM_006545.1; NM_021145.1; NM_002437.1; NM_006286; NM_001003698 /// NM_001003699 /// NM_002955; NM_001123 /// NM_006721; NM_024824; NM_004935.1; NM_002853.1; NM_019067.1; NM_024917.1; NM_020979.1; NM_005597.1; NM_007031.1; NM_009590.1; NM_020217.1; NM_025026.1; NM_014709.1; NM_014896.1; AF010144; NM_005374.1; NM_001696; NM_005494 /// NM_058246; NM_006534 /// NM_181659; NM_006368; NM_002268 /// NM_032771; NM_014033; NM_016138; NM_007048 /// NM_194441; NM_006694; NM_000051 /// NM_138292 /// NM_138293; NM_000410 /// NM_139002 /// NM_139003 /// NM_139004 /// NM_139005 /// NM_139006 /// NM_139007 /// NM_139008 /// NM_139009 /// NM_139010 /// NM_139011; NM_004691; NM_012070 /// NM_139321 /// NM_139322; NM_006095; NM_021814; NM_005547.1; NM_203458; NM_015547 /// NM_147161; AB007958.1; NM_207488; NM_005809 /// NM-181737 /// NM_181738; NM_016248 /// NM_144490; AK022213.1; NM_005708; NM_207102; AK023895; NM_144606 /// NM_144997; NM_018530; AK021474; U43604.1; AU147017; AF222691.1; NM_015116; NM_001005375 /// NM_001005785 /// NM_001005786 /// NM_004081 /// NM_020363 /// NM_020364 /// NM_020420; AC004692; NM_001014; NM_000585 /// NM_172174 /// NM_172175; NM_054020 /// NM_172095 /// NM_172096 /// NM_172097; BE466926; NM_018011; NM_024077; NM_012394; NM_019011 /// NM_207111 /// NM_(—)207116; NM_017646; NM_021800; NK_016049; NM_014395; NM_014336; NM_018097; NM_019014; NM_024804; NK_018260; NM_018118; NM_014128; NK_024084; NM_005294; AF077053; NM_138387; NM_024531; NM_000693; NK_018509; NM_033128; NM_020706; AI523613; and NM_014884

In one embodiment, the invention provides a gene group the expression profile of which is useful in diagnosing lung diseases and comprises probes that hybridize to at least, for example, 5, 10, 15, 20, 25, 30, 35, at least about 36, at least to 40, at least to 50, at least to 60, to at least 70, to at least 80, to all of the following 84 gene sequences: NM_030757.1; R83000; AK021571.1; NM_014182.1; NM_17932.1; U85430.1; AI683552; BC002642.1; AW024467; NM_030972.1; BC021135.1; AL161952.1; AK026565.1; AK023783.1; BF218804; NM_001281.1; NM_024006.1; AK023843.1; BC001602.1; BC034707.1; BC064619.1; AY280502.1; BC059387.1; AF135421.1; BC061522.1; L76200.1; U50532.1; BC006547.2; BC008797.2; BC000807.1; AL080112.1; BC033718.1 /// BC046176.1 /// BC038443.1; NM_000346.1; BC008710.1; Hs.288575 (UNIGENE ID); AF020591.1; BC000423.2; BC002503.2; BC008710.1; BC009185.2; Hs.528304 (UNIGENE ID); U50532.1; BC013923.2; BC031091; NM_007062; Hs.249591 (Unigene ID); BC075839.1 /// BG073760.1; BC072436.1 ///BC004560.2; BC001016.2; Hs.286261 (Unigene ID); AF348514.1; BC005023.1; BC066337.1 ///BC058736.1 /// BC050555.1; Hs.216623 (Unigene ID); BC072400.1; BC041073.1; U43965.1; BC021258.2; BC016057.1; BC016713.1 /// BC014535.1 /// AF237771.1; BC000360.2; BC007455.2; BC000701.2; BC010067.2; BC023528.2 /// BC047680.1; BC064957.1; Hs.156701 (Unigene ID); BC030619.2; BC008710.1; U43965.1; BC066329.1; Hs.438867 (Unigene ID); BC035025.2 /// BC050330.1; BC023976.2; BC074852.2 /// BC074851.2; Hs.445885 (Unigene ID); BC008591.2 /// BC050440.1 ///; BC048096.1; AF365931.1; AF257099.1; and BC028912.1.

In one embodiment, the invention provides a gene group the expression profile of which is useful in diagnosing lung diseases and comprises probes that hybridize to at least, for example 5,10, 15, 20, 25, 30, preferably at least about 36, still more preferably at least to 40, still more preferably at least to 45, still more preferably all of the following 50 gene sequences, although it can include any and all members, for example, 20, 21, 22, up to and including 36: NM_007062.1; NM_001281.1; BC000120.1; NM_014255.1; BC002642.1; NM_000346.1; NM_006545.1; BG034328; NM_021822.1; NM_021069.1; NM_019067.1; NM_017925.1; NM_017932.1; NM_030757.1; NM_030972.1; AF126181.1; U93240.1; U90552.1; AF151056.1; U85430.1; U51007.1; BC005969.1; NM_002271.1; AL566172; AB014576.1; BF218804; AK022494.1; AA114843; BE467941; NM_003541.1; R83000; AL161952.1; AK023843.1; AK021571.1; AK023783.1; AU147182; AL080112.1; AW971983; AI683552; NM_024006.1; AK026565.1; NM_014182.1; NM_021800.1; NM_016049.1; NM_019023.1; NM_021971.1; NM_014128.1; AK025651.1; AA133341; and AF198444.1. In one preferred embodiment, one can use at least 20-30, 30-40, of the 50 genes that overlap with the individual predictor genes identified in the analysis using the t-test, and, for example, 5-9 of the non-overlapping genes, identified using the t-test analysis as individual predictor genes, and combinations thereof.

In one embodiment, the invention provides a gene group the expression profile of which is useful in diagnosing lung diseases and comprises probes that hybridize to at least for example 5, 10, 15, 20, preferably at least about 25, still more preferably at least to 30, still more preferably all of the following 36 gene sequences: NM_007062.1; NM_001281.1; BC002642.1; NM_000346.1; NM_006545.1; BG034328; NM_019067.1; NM_017925.1; NM_017932.1; NM_030757.1; NM_030972.1; NM-002268 /// NM_032771; NM_007048 /// NM_194441; NM_006694; U85430.1; NM_004691; AB014576.1; BF218804; BE467941; R83000; AL161952.1; AK023843.1; AK021571.1; AK023783.1; AL080112.1; AW971983; AI683552; NM_024006.1; AK026565.1; NM_014182.1; NM_021800.1; NM_016049.1; NM_021971.1; NM_014128.1; AA133341; and AF198444.1. In one preferred embodiment, one can use at least 20 of the 36 genes that overlap with the individual predictors and, for example, 5-9 of the non-overlapping genes, and combinations thereof.

The expression of the gene groups in an individual sample can be analyzed using any probe specific to the nucleic acid sequences or protein product sequences encoded by the gene group members. For example, in one embodiment, a probe set useful in the methods of the present invention is selected from the nucleic acid probes of between 10-15, 15-20, 20-180, preferably between 30-180, still more preferably between 36-96, still more preferably between 36-84, still more preferably between 36-50 probes, included in the Affymetrix Inc. gene chip of the Human Genome U133 Set and identified as probe ID Nos: 208082_x_at, 214800_x_at, 215208_x_at, 218556_at, 207730_x_at, 210556_at, 217679_x_at, 202901_x_at, 213939_s_at, 208137_x_at, 214705_at, 215001_s_at, 218155_x_at, 215604_x_at, 212297_at, 201804_x_at, 217949_s_at, 215179_x_at, 211316_x_at, 217653_x_at, 266_s_at, 204718_at, 211916_s_at, 215032_at, 219920_s_at, 211996_s_at, 200075_s_at, 214753_at, 204102_s_at, 202419_at, 214715_x_at, 216859_x_at, 215529_x_at, 202936_s_at, 212130_x_at, 215204_at, 218735_s_at, 200078_s_at, 203455_s_at, 212227_x_at, 222282_at, 219678_x_at, 208268_at, 221899_at, 213721_at, 214718_at, 201608_s_at, 205684_s_at, 209008_x_at, 200825_s_at, 218160_at, 57739_at, 211921_x_at, 218074_at, 200914_x_at, 216384_x_at, 214594_x_at, 222122_s_at, 204060_s_at, 215314_at, 208238_x_at, 210705_s_at, 211184_s_at, 215418_at, 209393_s_at, 210101_x_at, 212052_s_at, 215011_at, 221932_s_at, 201239_s_at, 215553_x_at, 213351_s_at, 202021_x_at, 209442_x_at, 210131_x_at, 217713_x_at, 214707_x_at, 203272_s_at, 206279_at, 214912_at, 201729_s_at, 205917_at, 200772_x_at, 202842_s_at, 203588_s_at, 209703_x_at, 217313_at, 217588_at, 214153_at, 222155_s_at, 203704_s_at, 220934_s_at, 206929_s_at, 220459_at, 215645_at, 217336_at, 203301_s_at, 207283_at, 222168_at, 222272_x_at, 219290_x_at, 204119_s_at, 215387_x_at, 222358_x_at, 205010_at, 1316_at, 216187_x_at, 208678_at, 222310_at, 210434_x_at, 220242_x_at, 207287_at, 207953_at, 209015_s_at, 221759_at, 220856_x_at, 200654_at, 220071_x_at, 216745_x_at, 218976_at, 214833_at, 202004_x_at, 209653_at, 210858_x_at, 212041_at, 221294_at, 207020_at, 204461_x_at, 207984_s_at, 215373_x_at, 216110_x_at, 215600_x_at, 216922_x_at, 215892_at, 201530_x_at, 217371_s_at, 222231_s_at, 218265_at, 201537_s_at, 221616_s_at, 213106_at, 215336_at, 209770_at, 209061_at, 202573_at, 207064_s_at, 64371_at, 219977_at, 218617_at, 214902_x_at, 207436_x_at, 215659_at, 204216_s_at, 214763_at, 200877_at, 218425_at, 203246_s_at, 203466_at, 204247_s_at, 216012_at, 211328_x_at, 218336_at, 209746_s_at, 214722_at, 214599_at, 220113_x_at, 213212_x_at, 217671_at, 207365_x_at, 218067_s_at, 205238_at, 209432_s_at, and 213919_at. In one preferred embodiment, one can use at least, for example, 10-20, 20-30, 30-40, 40-50, 50-60, 60-70, 70-80, 80-90, 90-100, 110, 120, 130, 140, 150, 160, or 170 of the 180 genes that overlap with the individual predictors genes and, for example, 5-9 of the non-overlapping genes and combinations thereof.

Sequences for the Affymetrix probes are provided in the Appendix to the specification, all the pages of which are herein incorporated by reference in their entirety.

One can analyze the expression data to identify expression patters associated with any lung disease that is caused by exposure to air pollutants, such as cigarette smoke, asbestos or any other lung disease. For example, the analysis can be performed as follows. One first scans a gene chip or mixture of beads comprising probes that are hybridized with a study group samples. For example, one can use samples of non-smokers and smokers, non-asbestos exposed individuals and asbestos-exposed individuals, non-smog exposed individuals and smog-exposed individuals, smokers without a lung disease and smokers with lung disease, to obtain the differentially expressed gene groups between individuals with no lung disease and individuals with lung disease. One must, of course select appropriate groups, wherein only one air pollutant can be selected as a variable. So, for example, one can compare non-smokers exposed to asbestos but not smog and non-smokers not exposed to asbestos or smog.

The obtained expression analysis, such as microarray or microbead raw data consists of signal strength and detection p-value. One normalizes or scales the data, and filters the poor quality chips/bead sets based on images of the expression data, control probes, and histograms. One also filters contaminated specimens which contain non-epithelial cells. Lastly, one filters the genes of importance using detection p-value. This results in identification of transcripts present in normal airways (normal airway transcriptome). Variability and multiple regression analysis can be used. This also results in identification of effects of smoking on airway epithelial cell transcription. For this analysis, one can use T-test and Pearson correlation analysis. One can also identify a group or a set of transcripts that are differentially expressed in samples with lung disease, such as lung cancer and samples without cancer. This analysis was performed using class prediction models.

For analysis of the data, one can use, for example, a weighted voting method. The weighted voting method ranks, and gives a weight “p” to all genes by the signal to noise ration of gene expression between two classes: P=mean_((class 1))−mean_((class2))sd_((class 1))=sd_((class 2)). Committees of variable sizes of the top ranked genes are used to evaluate test samples, but genes with more significant p-values can be more heavily weighed. Each committee genes in test sample votes for one class or the other, based on how close that gene expression level is to the class 1 mean or the class 2 mean. V_((gene A))=P_((gene A)), i.e. level of expression in test sample less the average of the mean expression values in the two classes. Votes for each class are tallied and the winning class is determined along with prediction strength as PS=V_(win)−V_(lose)/V_(win)+V_(lose). Finally, the accuracy can be validated using cross-validation +/− independent samples.

Table 1 shows 96 genes that were identified as a group distinguishing smokers with cancer from smokers without cancer. The difference in expression is indicated at the column on the right as either “down”, which indicates that the expression of that particular transcript was lower in smokers with cancer than in smokers without cancer, and “up”, which indicates that the expression of that particular transcript was higher in smokers with cancer than smokers without cancer. In one embodiment, the exemplary probes shown in the column “Affymetrix Id in the Human Genome U133 chip” can be used. Sequences for the Affymetrix probes are provided in the Appendix.

TABLE 1 96 Gene Group Direction Affymetrix Id GenBank ID Gene Description Gene Name in Cancer 1316_at NM_003335 ubiquitin-activated enzyme E1-like UBE1L down 200654_at NM_000918 procollagen-proline, 2-oxoglutarate 4-dioxygenase (proline P4HB up 4-hydroxylase), beta polypeptide (protein disulfide isomerase; thyroid hormone binding protein p55) 200877_at NM_006430.1 chaperonin containing TCP1, subunit 4 (delta) CCT4 up 201530_x_at NM_001416.1 eukaryotic translation factor 4A, isoform 1 EIF4A1 up 201537_s_at NM_004090 dual specificity phosphatase 3 (vaccinia virus phosphatase VH1-related) DUSP3 up 201923_at NM_006406.1 peroxiredoxin 4 PRDX4 up 202004_x_at NM_003001.2 succinate dehydrogenase complex, subunit C, integral membrane protein, SDHC up 15 kDa 202573_at NM_001319 casein kinase 1, gamma 2 CSNK1G2 down 203246_s_at NM_006545.1 tumor suppressor candidate 4 TUSC4 up 203301_s_at NM_021145.1 cyclin D binding myb-like transcription factor 1 DMTF1 down 203466_at NM_002437.1 MpV17 transgene, murine homolog, glomerusclerosis MPV17 up 203588_s_at NM_006286 transcription factor Dp-2 (E2F dimerization partner 2) TFDP2 up 203704_s_at NM_001003698 /// ras responsive element binding protein 1 RREB1 down NM_001003699 /// NM_002955 204119_s_at NM_001123 /// adenosine kinase ADK up NM_006721 204216_s_at NM_024824 nuclear protein UKp68 FLJ11806 up 204247_s_at NM_004935.1 cyclin-dependent kinase 5 CDK5 up 204461_x_at NM_002853.1 RAD1 homolog RAD1 down 205010_at NM_019067.1 hypothetical protein FLJ10613 FLJ10613 down 205238_at NM_024917.1 chromosome X open reading frame 34 CXorf34 down 205367_at NM_020979.1 adaptor protein with pleckstrin homology and src homology 2 domains APS down 206929_s_at NM_005597.1 nuclear factor I/c (CCAAT-binding transcription factor) NFIC down 207020_at NM_007031.1 heat shock transcription factor 2 binding protein HSF2BP down 207064_s_at NM_009590.1 amine oxidase, copper containing 2 (retina-specific) AOC2 down 207283_at NM_020217.1 hypothetical protein DKFZp547I014 DKFZp547I014 down 207287_at NM_025026.1 hypothetical protein FLJ14107 FLJ14107 down 207365_x_at NM_014709.1 ubiquitin specific protease 34 USP34 down 207436_x_at NM_014896.1 KIAA0894 protein KIAA0894 down 207953_at AF010144 — — down 207984_s_at NM_005374.1 membrane protein, palmitoylated 2 (MAGUK p55 subfamily member2 MPP2 down 208678_at NM_001696 ATPase, H+ transporting, lysosomal 31 kDa, V1 subunit E, isoform 1 ATP6V1E1 up 209015_s_at NM_005494 /// DnaJ (Hsp40) homolog, subfamily B, member 6 DNAJB6 up NM_058246 209061_at NM_006534 /// nuclear receptor coactivator 3 NCOA3 down NM_181659 209432_s_at NM_006368 cAMP responsive element binding protein 3 CREB3 up 209653_at NM_002268 /// karyopherin alpha 4 (importin alpha 3) KPNA4 up NM_032771 209703_x_at NM_014033 DKFZP586A0522 protein DKFZP586A0522 down 209746_s_at NM_016138 coenzyme Q7 homolog, ubiquinone COQ7 down 209770_at NM_007048 /// butyrophilin, subfamily 3, member A1 BTN3A1 down NM_194441 210434_x_at NM_006694 jumping translocation breakpoint JTB up 210858_x_at NM_000051 /// ataxia telangiectasia mutated (includes complementation groups A, C, and D ATM down NM_138292 /// NM_138293 211328_x_at NM_000410 /// hemochromatosis HFE down NM_139002 /// NM_139003 /// NM_139004 /// NM_139005 /// NM_139006 /// NM_139007 /// NM_139008 /// NM_139009 /// NM_139010 /// NM_139011 212041_at NM_004691 ATPase, H+ transporting, lysosomal 38 kDa, V0 subunit d isoform 1 ATP6V0D1 up 212517_at NM_012070 /// attractin ATRN down NM_139321 /// NM_139322 213106_at NM_006095 ATPase, aminophospholipid transporter (APLT), Class I, type 8A, member 1 ATP8A1 down 213212_x_at AI632181 Similar to FLJ40113 protein — down 213919_at AW024467 — — down 214153_at NM_021814 ELOVL family member 5, elongation of long chain fatty acids (FEN1/Elo2, ELOVL5 down SUR4/Elo3-like, yeast) 214599_at NM_005547.1 involucrin IVL down 214722_at NM_203458 similar to NOTCH2 protein N2N down 214763_at NM_015547 /// thiosterase, adipose associated THEA down NM_147161 214833_at AB007958.1 KIAA0792 gene product KIAA0792 down 214902_x_at NM_207488 FLJ42393 protein FLJ42393 down 215067_x_at NM_005809 /// peroxiredoxin 2 PRDX2 down NM_181737 /// NM_181738 215336_at NM_016248 /// A kinase (PRKA) anchor protein AKAP11 down NM_144490 215373_x_at AK022213.1 hypothetical protein FLJ12151 FLJ12151 down 215387_x_at NM_005708 Glypican 6 GPC6 down 215600_x_at NM_207102 F-box and WD-40 domain protein 12 FBXW12 down 215609_at AK023895 — — down 215645_at NM_144606 /// Hypothetical protein MGC13008 FLCN down NM_144997 215659_at NM_018530 Gasdermin-like GSDML down 215892_at AK021474 — — down 216012_at U43604.1 human unidentified mRNA, partial sequence — down 216110_x_at AU147017 — — down 216187_x_at AF222691.1 Homo sapiens Alu repeat LNX1 down 216745_x_at NM_015116 Leucine-rich repeats and calponin homology (CH) domain containing 1 LRCH1 down 216922_x_at NM_001005375 /// deleted in azoospermia DAZ2 down NM_001005785 /// NM_001005786 /// NM_004081 /// NM_020363 /// NM_020364 /// NM_020420 217313_at AC004692 — — down 217336_at NM_001014 ribosomal protein S10 RPS10 down 217371_s_at NM_000585 /// interleukin 15 IL15 down NM_172174 /// NM_172175 217588_at NM_054020 /// cation channel, sperm associated 2 CATSPER2 down NM_172095 /// NM_172096 /// NM_172097 217671_at BE466926 — — down 218067_s_at NM_018011 hypothetical protein FLJ10154 FLJ10154 down 218265_at NM_024077 SECIS binding protein 2 SECISBP2 down 218336_at NM_012394 prefoldin 2 PFDN2 up 218425_at NM_019411 /// TRIAD3 protein TRIAD3 down NM_207111 /// NM_207116 218617_at NM_017646 tRNA isopentenyltransferase 1 TRIT1 down 218976_at NM_021800 DnaJ (Hsp40) homolog, subfamily C, member 12 DNAJC12 up 219203_at NM_016049 chromosome 14 open reading frame 122 C14orf122 up 219290_x_at NM_014395 dual adaptor of phosphotyrosine and 3-phosphoinositides DAPP1 down 219977_at NM_014336 aryl hydrocarbon receptor interacting protein-like 1 AIPL1 down 220071_x_at NM_018097 chromosome 15 open reading frame 25 C15orf25 down 220113_x_at NM_019014 polymerase (RNA) I polypeptide B, 128 kDa POLR1B down 210215_at NM_024804 hypothetical protein FLJ12606 FLJ12606 down 220242_x_at NM_018260 hypothetical protein FLJ10891 FLJ10891 down 220459_at NM_018118 MCM3 minichromosome maintenace deficient 3 (s. cerevisiae) associated MCM3APAS down protein, antisense 220856_x_at NM_014128 — down 220934_s_at NM_024084 hypothetical protein MGC3196 MGC3196 down 221294_at NM_005294 G protein-coupled receptor 21 GPR21 down 221616_s_at AF077053 Phosphoglycerate kinase 1 PGK1 down 221759_at NM_138387 glucose-6-phosphatase catalytic subunit-related G6PC3 up 222155_s_at NM_024531 G protein-coupled receptor 172A GPR172A up 222168_at NM_000693 Aldehyde dehydrogenase 1 family, member A3 ALDH1A3 down 222231_s_at NM_018509 hypothetical protein PRO1855 PRO1855 up 222272_x_at NM_033128 scinderin SCIN down 222310_at NM_020706 splicing factor, arginine/serine-rich 15 SFRS15 down 222358_x_at AI523613 — — down 64371_at NM_014884 splicing factor, arginine/serine-rich 14 SFRS14 down

Table 2 shows one preferred 84 gene group that was identified as a group distinguishing smokers with cancer from smokers without cancer. The difference in expression is indicated at the column on the right as either “down”, which indicates that the expression of that particular transcript was lower in smokers with cancer than in smokers without cancer, and “up”, which indicates that the expression of that particular transcript was higher in smokers with cancer than smokers without cancer. These genes were identified using traditional Student's t-test analysis.

In one embodiment, the exemplary probes shown in the column “Affymetrix Id in the Human Genome U133 chip” can be used in the expression analysis.

TABLE 2 84 Gene Group GenBank ID (unless otherwise Direction mentioned) Gene Name Description in Cancer Affymetrix ID NM_030757.1 MKRN4 makorin, ring finger protein, 4 /// makorin, ring finger protein, 4 down 208082_x_at R83000 BTF3 basic transcription factor 3 down 214800_x_at AK021571.1 MUC20 mucin 20 down 215208_x_at NM_014182.1 ORMDL2 ORM1-like 2 (S. cerevisiae) up 218556_at NM_17932.1 FLJ20700 hypothetical protein FLJ20700 down 207730_x_at U85430.1 NFATC3 nuclear factor of activated T-cells, cytoplasmic, calcineurin-dependent 3 down 210556_at AI683552 — — down 217679_x_at BC002642.1 CTSS cathepsin S down 202901_x_at AW024467 RIPX rap2 interacting protein x down 213939_s_at NM_030972.1 MGC5384 hypothetical protein MGC5384 /// hypothetical protein MGC5384 down 208137_x_at BC021135.1 INADL InaD-like protein down 214705_at AL161952.1 GLUL glutamate-ammonia ligase (glutamine synthase) down 215001_s_at AK026565.1 FLJ10534 hypothetical protein FLJ10534 down 218155_x_at AK023783.1 — Homo sapiens cDNA FLJ13721 fis, clone PLACE2000450. down 215604_x_at BF218804 AFURS1 ATPase family homolog up-regulated in senescence cells down 212297_at NM_001281.1 CKAP1 cytoskeleton associated protein 1 up 201804_x_at NM_024006.1 IMAGE3455200 hypothetical protein IMAGE3455200 up 217949_s_at AK023843.1 PGF placental growth factor, vascular endothelial growth factor-related protein down 215179_x_at BC001602.1 CFLAR CASP8 and FADD-like apoptosis regulator down 211316_x_at BC034707.1 — Homo sapiens transcribed sequence with weak similarity to protein ref: down 217653_x_at NP_060312.1 (H. sapiens) hypothetical protein FLJ20489 [Homo sapiens] BC064619.1 CD24 CD24 antigen (small cell lung carcinoma cluster 4 antigen) down 266_s_at AY280502.1 EPHB6 EphB6 down 204718_at BC059387.1 MYO1A myosin IA down 211916_s_at — Homo sapiens transcribed sequences down 215032_at AF135421.1 GMPPB GDP-mannose pyrophosphorylase B up 219920_s_at BC061522.1 MGC70907 similar to MGC9515 protein down 211996_s_at L76200.1 GUK1 guanylate kinase 1 up 200075_s_at U50532.1 CG005 hypothetical protein from BCRA2 region down 214753_at BC006547.2 EEF2 eukaryotic translation elongation factor 2 down 204102_s_at BC008797.2 FVT1 follicular lymphoma variant translocation 1 down 202419_at BC000807.1 ZNF160 zinc finger protein 160 down 214715_x_at AL080112.1 — — down 216859_x_at BC033718.1 /// C21orf106 chromosome 21 open reading frame 106 down 215529_x_at BC046176.1 /// BC038443.1 NM_000346.1 SOX9 SRY (sex determining region Y)-box 9 (campomelic dysplasia, autosomal up 202936_s_at sex-reversal) BC008710.1 SUI1 putative translation initiation factor up 212130_x_at Hs.288575 — Homo sapiens cDNA FLJ14090 fis, clone MAMMA1000264. down 215204_at (UNIGENE ID) AF020591.1 AF020591 zinc finger protein down 218735_s_at BC000423.2 ATP6V0B ATPase, H+ transporting, lysosomal 21 kDa, V0 subunit c″ /// ATPase, H+ up 200078_s_at transporting, lysosomal 21 kDa, V0 subunit c″ BC002503.2 SAT spermidine/spermine N1-acetyltransferase down 203455_s_at BC008710.1 SUI1 putative translation initiation factor up 212227_x_at — Homo sapiens transcribed sequences down 222282_at BC009185.2 DCLRE1C DNA cross-link repair 1C (PSO2 homolog, S. cerevisiae) down 219678_x_at Hs.528304 ADAM28 a disintegrin and metalloproteinase domain 28 down 208268_at (UNIGENE ID) U50532.1 CG005 hypothetical protein from BCRA2 region down 221899_at BC013923.2 SOX2 SRY (sex determining region Y)-box 2 down 213721_at BC031091 ODAG ocular development-associated gene down 214718_at NM_007062 PWP1 nuclear phosphoprotein similar to S. cerevisiae PWP1 up 201608_s_at Hs.249591 FLJ20686 hypothetical protein FLJ20686 down 205684_s_at (Unigene ID) BC075839.1 /// KRT8 keratin 8 up 209008_x_at BC073760.1 BC072436.1 /// HYOU1 hypoxia up-regulated 1 up 200825_s_at BC004560.2 BC001016.2 NDUFA8 NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 8, 19 kDa up 218160_at Hs.286261 FLJ20195 hypothetical protein FLJ20195 down 57739_at (Unigene ID) AF348514.1 — Homo sapiens fetal thymus prothymosin alpha mRNA, complete cds down 211921_x_at BC005023.1 CGI-128 CGI-128 protein up 218074_at BC066337.1 /// KTN1 kinectin 1 (kinesin receptor) down 200914_x_at BC058736.1 /// BC050555.1 — — down 216384_x_at Hs.216623 ATP8B1 ATPase, Class I, type 8B, member 1 down 214594_x_at (Unigene ID) BC072400.1 THOC2 THO complex 2 down 222122_s_at BC041073.1 PRKX protein kinase, X-linked down 204060_s_at U43965.1 ANK3 ankyrin 3, node of Ranvier (ankyrin G) down 215314_at — — down 208238_x_at BC021258.2 TRIM5 tripartite motif-containing 5 down 210705_s_at BC016057.1 USH1C Usher syndrome 1C (autosomal recessive, severe) down 211184_s_at BC016713.1 /// PARVA parvin, alpha down 215418_at BC014535.1 /// AF237771.1 BC000360.2 EIF4EL3 eukaryotic translation initiation factor 4E-like 3 up 209393_s_at BC007455.2 SH3GLB1 SH3-domain GRB2-like endophilin B1 up 210101_x_at BC000701.2 KIAA0676 KIAA0676 protein down 212052_s_at BC010067.2 CHC1 chromosome condensation 1 down 215011_at BC023528.2 /// C14orf87 chromosome 14 open reading frame 87 up 221932_s_at BC047680.1 BC064957.1 KIAA0102 KIAA0102 gene product up 201239_s_at Hs.156701 — Homo sapiens cDNA FLJ14253 fis, clone OVARC1001376. down 215553_x_at (Unigene ID) BC030619.2 KIAA0779 KIAA0779 protein down 213351_s_at BC008710.1 SUI1 putative translation initiation factor up 202021_x_at U43965.1 ANK3 ankyrin 3, node of Ranvier (ankyrin G) down 209442_x_at BC066329.1 SDHC succinate dehydrogenase complex, subunit C, integral membrane protein, 15 kDa up 210131_x_at Hs.438867 — Homo sapiens transcribed sequence with weak similarity to protein ref: down 217713_x_at (Unigene ID) NP_060312.1 (H. sapiens) hypothetical protein FLJ20489 [Homo sapiens] BC035025.2 /// ALMS1 Alstrom syndrome 1 down 214707_x_at BC050330.1 BC023976.2 PDAP2 PDGFA associated protein 2 up 203272_s_at BC074852.2 /// PRKY protein kinase, Y-linked down 206279_at BC074851.2 Hs.445885 KIAA1217 Homo sapiens cDNA FLJ12005 fis, clone HEMBB1001565. down 214912_at (Unigene ID) BC008591.2 /// KIAA0100 KIAA0100 gene product up 201729_s_at BC050440.1 /// BC048096.1 AF365931.1 ZNF264 zinc finger protein 264 down 205917_at AF257099.1 PTMA prothymosin, alpha (gene sequence 28) down 200772_x_at BC028912.1 DNAJB9 DnaJ (Hsp40) homolog, subfamily B, member 9 up 202842_s_at

Table 3 shows one preferred 50 gene group that was identified as a group distinguishing smokers with cancer from smokers without cancer. The difference in expression is indicated at the column on the right as either “down”, which indicates that the expression of that particular transcript was lower in smokers with cancer than in smokers without cancer, and “up”, which indicates that the expression of that particular transcript was higher in smokers with cancer than smokers without cancer.

This gene group was identified using the GenePattern server from the Broad institute, which includes the Weighted Voting algorithm. The default settings, i.e., the signal to noise ratio and no gene filtering, were used,

In one embodiment, the exemplary probes shown in the column “Asymetrix Id in the Human Genome U133 chip” can be used in the expression analysis.

TABLE 3 50 Gene Group Affymetrix Id in Direction the Human Genome GenBank ID Gene Name in Cancer U133 chip NM_007062.1 PWP1 up in cancer 201608_s_at NM_001281.1 CKAP1 up in cancer 201804_x_at BC000120.1 up in cancer 202355_s_at NM_014255.1 TMEM4 up in cancer 202857_at BC002642.1 CTSS up in cancer 202901_x_at NM_000346.1 SOX9 up in cancer 202936_s_at NM_006545.1 NPR2L up in cancer 203246_s_at BG034328 up in cancer 203588_s_at NM_021822.1 APOBEC3G up in cancer 204205_at NM_021069.1 ARGBP2 up in cancer 204288_s_at NM_019067.1 FLJ10613 up in cancer 205010_at NM_017925.1 FLJ20686 up in cancer 205684_s_at NM_017932.1 FLJ20700 up in cancer 207730_x_at NM_030757.1 MKRN4 up in cancer 208082_x_at NM_030972.1 MGC5384 up in cancer 208137_x_at AF126181.1 BCG1 up in cancer 208682_s_at U93240.1 up in cancer 209653_at U90552.1 up in cancer 209770_at AF151056.1 up in cancer 210434_x_at U85430.1 NFATC3 up in cancer 210556_at U51007.1 up in cancer 211609_x_at BC005969.1 up in cancer 211759_x_at NM_002271.1 up in cancer 211954_s_at AL566172 up in cancer 212041_at AB014576.1 KIAA0676 up in cancer 212052_s_at BF218804 AFURS1 down in cancer 212297_at AK022494.1 down in cancer 212932_at AA114843 down in cancer 213884_s_at BE467941 down in cancer 214153_at NM_003541.1 HIST1H4K down in cancer 214463_x_at R83000 BTF3 down in cancer 214800_x_at AL161952.1 GLUL down in cancer 215001_s_at AK023843.1 PGF down in cancer 215179_x_at AK021571.1 MUC20 down in cancer 215208_x_at AK023783.1 — down in cancer 215604_x_at AU147182 down in cancer 215620_at AL080112.1 — down in cancer 216859_x_at AW971983 down in cancer 217588_at AI683552 — down in cancer 217679_x_at NM_024006.1 IMAGE3455200 down in cancer 217949_s_at AK026565.1 FLJ10534 down in cancer 218155_x_at NM_014182.1 ORMDL2 down in cancer 218556_at NM_021800.1 DNAJC12 down in cancer 218976_at NM_016049.1 CGI-112 down in cancer 219203_at NM_019023.1 PRMT7 down in cancer 219408_at NM_021971.1 GMPPB down in cancer 219920_s_at NM_014128.1 — down in cancer 220856_x_at AK025651.1 down in cancer 221648_s_at AA133341 C14orf87 down in cancer 221932_s_at AF198444.1 down in cancer 222168_at

Table 4 shows one preferred 36 gene group that was identified as a group distinguishing smokers with cancer from smokers without cancer. The difference in expression is indicated at the column on the right as either “down”, which indicates that the expression of that particular transcript was lower in smokers with cancer than in smokers without cancer, and “up”, which indicates that the expression of that particular transcript was higher in smokers with cancer than smokers without cancer.

In one embodiment the exemplary probes shown in the column “Affymetrix Id in the Human Genome U133 chip” can be used in the expression analysis.

TABLE 4 36 Gene Group GenBank ID Gene Name Gene Description Affy ID NM_007062.1 PWP1 nuclear phosphoprotein similar to S. cerevisiae PWP1 201608_s_at NM_001281.1 CKAP1 cytoskeleton associated protein 1 201804_x_at BC002642.1 CTSS cathepsin S 202901_x_at NM_000346.1 SOX9 SRY (sex determining region Y)-box 9 (campomelic dysplasia, autosomal sex-reversal) 202936_s_at NM_006545.1 NPR2L homologous to yeast nitrogen permease (candidate tumor suppressor) 203246_s_at BG034328 transcription factor Dp-2 (E2F dimerization partner 2) 203588_s_at NM_019067.1 FLJ10613 hypothetical protein FLJ10613 205010_at NM_017925.1 FLJ20686 hypothetical protein FLJ20686 205684_s_at NM_017932.1 FLJ20700 hypothetical protein FLJ20700 207730_x_at NM_030757.1 MKRN4 makorin, ring finger protein, 4 /// makorin, ring finger protein, 4 208082_x_at NM_030972.1 MGC5384 hypothetical protein MGC5384 208137_x_at NM_002268 /// KPNA4 karyopherin alpha 4 (importin alpha 3) 209653_at NM_032771 NM_007048 /// BTN3A1 butyrophilin, subfamily 3, member A1 209770_at NM_194441 NM_006694 JBT jumping translocation breakpoint 210434_x_at U85430.1 NFATC3 nuclear factor of activated T-cells, cytoplasmic, calcineurin-dependent 3 210556_at NM_004691 ATP6V0D1 ATPase, H+ transporting, lysosomal 38 kDa, V0 subunit d isoform 1 212041_at AB014576.1 KIAA0676 KIAA0676 protein 212052_s_at BF218804 AFURS1 ATPase family homolog up-regulated in senescence cells 212297_at BE467941 EVOVL family member 5, elongation of long chain fatty acids (FEN1/Elo2, 214153_at SUR4/Elo3-like, yeast) R83000 BTF3 basic transcription factor 3 214800_x_at AL161952.1 GLUL glutamate-ammonia ligase (glutamine synthase) 215001_s_at AK023843.1 PGF placental growth factor, vascular endothelial growth factor-related protein 215179_x_at AK021571.1 MUC20 mucin 20 215208_x_at AK023783.1 — Homo sapiens cDNA FLJ13721 fis, clone PLACE2000450. 215604_x_at AD080112.1 — — 216859_x_at AW971983 cation, sperm associated 2 217588_at AI683552 — — 217679_x_at NM_024006.1 IMAGE3455200 hypothetical protein IMAGE3455200 217949_s_at AK026565.1 FLJ10534 hypothetical protein FLJ10534 218155_x_at NM_014182.1 ORMDL2 ORM1-like 2 (S. cerevisiae) 218556_at NM_021800.1 DNAJC12 J Domain containing protein 1 218976_at NM_016049.1 CGI-112 comparative gene identification transcript 112 219203_at NM_021971.1 GMPPB GDP-mannose pyrophosphorylase B 219920_s_at NM_014128.1 — — 220856_x_at AA133341 C14orf87 chromosome 14 open reading frame 87 221932_s_at AF198444.1 Homo sapiens 10q21 mRNA sequence 222168_at

In one embodiment, the gene group of the present invention comprises at least, for example, 5, 10, 15, 20, 25, 30, more preferably at least 36, still more preferably at least about 40, still more preferably at least about 50, still more preferably at least about 60, still more preferably at least about 70, still more preferably at least about 80, still more preferably at least about 86, still more preferably at least about 90, still more preferably at least about 96 of the genes as shown in Tables 1-4.

In one preferred embodiment, the gene group comprises 36-180 genes selected from the group consisting of the genes listed in Tables 1-4.

In one embodiment, the invention provides group of genes the expression of which is lower in individuals with cancer.

Accordingly, in one embodiment, the invention provides of a group of genes useful in diagnosing lung diseases, wherein the expression of the group of genes is lower in individuals exposed to air pollutants with cancer as compared to individuals exposed to the same air pollutant who do not have cancer, the group comprising probes that hybridize at least 5, preferably at least about 5-10, still more preferably at least about 10-20, still more preferably at least about 20-30, still more preferably at least about 30-40, still more preferably at least about 40-50, still more preferably at least about 50-60, still more preferably at least about 60-70, still more preferably about 72 genes consisting of transcripts (transcripts are identified using their GenBank ID or Unigene ID numbers and the corresponding gene names appear in Table 1): NM_003335; NM_001319; NM_021145.1; NM_001003698 /// NM_001003699 ///; NM_002955; NM_002853.1; NM_019067.1; NM_024917.1; NM_020979.1; NM_005597.1; NM_007031.1; NM_009590.1; NM_020217.1; NM_025026.1; NM_014709.1; NM_014896.1; AF010144; NM_005374.1; NM_006534 /// NM_181659; NM_014033; NM_016138; NM_007048 /// NM_194441; NM_000051 /// NM_138292 /// NM_138293; NM_000410 /// NM_139002 /// NM_139003 /// NM_139004 /// NM_139005 /// NM_139006 /// NM_139007 /// NM_139008 /// NM_139009 /// NM_139010 /// NM_139011; NM_012070 /// NM_139321 /// NM_139322; NM_006095; AI632181; AW024467; NM_021814; NM_005547.1; NM_203458; NM_015547 /// NM_147161; AB007958.1; NM_207488; NM_005809 /// NM_181737 /// NM_81738; NM_016248 /// NM_144490; AK022213.1; NM_005708; NM_207102; AK023895; NM_144606 /// NM_144997; NM_018530; AK021474; U43604.1; AU147017; AF222691.1; NM_015116; NM_01005375 /// NM_001005785 /// NM_001005786 /// NM_004081 /// NM_020363 /// NM_020364 /// NM_020420; AC004692; NM_001014; NM_000585 /// NM_172174 /// NM_172175; NM_054020 /// NM_172095 /// NM_172096 /// NM_172097; BE466926; NM_018011; NM_024077; NM_019011 /// NM_207111 /// NM_207116; NM_017646; NM_014395; NM_014336; NM_018097; NM_019014; NM_024804; NM_018260; NM_018118; NM_014128; NM_024084; NM_005294; AF077053; NM_000693; NM_033128; NM_020706; AI523613; and NM_014884.

In another embodiment, the invention provides of a group of genes useful in diagnosing lung diseases wherein the expression of the group of genes is lower in individuals exposed to air pollutants with cancer as compared to individuals exposed to the same air pollutant who do not have cancer, the group comprising probes that hybridize at least 5, preferably at least about 5-10, still more preferably at least about 10-20, still more preferably at least about 20-30, still more preferably at least about 30-40, still more preferably at least about 40-50, still more preferably at least about 50-60, still more preferably about 63 genes consisting of transcripts (transcripts are identified using their GenBank ID or Unigene ID numbers and the corresponding gene names appear in Table 2): NM_030757.1; R83000; AK021571.1; NM_17932.1; U85430.1; AI683552; BC002642.1; AW024467; NM_030972.1; BC021135.1; AL161952.1; AK026565.1; AK023783.1; BF218804; AK023843.1; BC001602.1; BC034707.1; BC064619.1; AY280502.1; BC059387.1; BC061522.1; U50532.1; BC006547.2; BC008797.2; BC000807.1; AL080112.1; BC033718.1 /// BC046176.1///; BC038443.1; Hs.288575 (UNIGENE ID); AF020591.1; BC002503.2; BC009185.2; Hs.528304 (UNIGENE ID); U50532.1; BC013923.2; BC031091; Hs.249591 (Unigene ID); Hs.286261 (Unigene ID); AF348514.1; BC066337.1 /// BC058736.1 /// BC050555.1; Hs.216623 (Unigene ID); BC072400.1; BC041073.1; U43965.1; BC021258.2; BC016057.1; BC016713.1 /// BC014535.1 /// AF237771.1; BC000701.2; BC010067.2; Hs.156701 (Unigene ID); BC030619.2; U43965.1; Hs.438867 (Unigene ID); BC035025.2 ///BC050330.1; BC074852.2/// BC074851.2; Hs.445885 (Unigene ID); AF365931.1; and AF257099.1

In another embodiment, the invention provides of a group of genes useful in diagnosing lung diseases wherein the expression of the group of genes is lower in individuals exposed to air pollutants with cancer as compared to individuals exposed to the same air pollutant who do not have cancer, the group comprising probes that hybridize at least 5, preferably at least about 5-10, still more preferably at least about 10-20, still more preferably at least about 20-25, still more preferably about 25 genes consisting of transcripts (transcripts are identified using their GenBank ID or Unigene ID numbers and the corresponding gene names appear in Table 3): BF218804; AK022494.1; AA114843; BE467941; NM_003541.1; R83000; AL161952.1; AK023843.1; AK021571.1; AK023783.1; AU147182; AL080112.1; AW971983; AI683552; NM_024006.1; AK026565.1; N_014182.1; NM_021800.1; NM_016049.1; NM_019023.1; NM_021971.1; NM_014128.1; AK025651.1; AA133341; and AF198444.1.

In another embodiment, the invention provides of a group of genes useful in diagnosing lung diseases wherein the expression of the group of genes is higher in individuals exposed to air pollutants with cancer as compared to individuals exposed to the same air pollutant who do not have cancer, the group comprising probes that hybridize at least to 5, preferably at least about 5-10, still more preferably at least about 10-20, still more preferably at least about 20-25, still more preferably about 25 genes consisting of transcripts (transcripts are identified using their GenBank ID or Unigene ID numbers and the corresponding gene names appear in Table 1): NM_000918; NM_006430.1; NM_001416.1; NM_004090; NM_006406.1; NM_003001.2; NM_006545.1; NM_002437.1; NM_006286; NM_001123 /// NM_006721; NM_024824; NM_004935.1; NM_001696; NM_005494 /// NM_058246; NM_006368; NM_002268 /// NM_032771; NM_006694; NM_004691; NM_012394; NM_021800; NM_016049; NM_138387; NM_024531; and NM_018509.

In another embodiment, the invention provides of a group of genes useful in diagnosing lung diseases wherein the expression of the group of genes is higher in individuals exposed to air pollutants with cancer as compared to individuals exposed to the same air pollutant who do not have cancer, the group comprising probes that hybridize at least to 5, preferably at least about 5-10, still more preferably at least about 10-20, still more preferably at least about 20-23, still more preferably about 23 genes consisting of transcripts (transcripts are identified using their GenBank ID or Unigene ID numbers and the corresponding gene names appear in Table 2): NM_014182.1; NM_001281.1; NM_024006.1; AF135421.1; L76200.1; NM_000346.1; BC008710.1; BC000423.2; BC008710.1; NM_007062; BC075839.1 /// BC073760.1; BC072436.1 /// BC004560.2; BC001016.2; BC005023.1; BC000360.2; BC007455.2; BC023528.2 /// BC047680.1; BC064957.1; BC008710.1; BC066329.1; BC023976.2; BC008591.2 /// BC050440.1 /// BC048096.1; and BC028912.1.

In another embodiment, the invention provides of a group of genes useful in diagnosing lung diseases wherein the expression of the group of genes is higher in individuals exposed to air pollutants with cancer as compared to individuals exposed to the same air pollutant who do not have cancer, the group comprising probes that hybridize at least about 5-10, still more preferably at least about 10-20, still more preferably at least about 20-25, still their GenBank ID or Unigene ID numbers and the corresponding gene names appear in Table 3); NM_007062.1; NM_001281.1; BC000120.1; NM_014255.1; BC002642.1; NM_000346.1; NM_006545.1; BG034328; NM_021822.1; NM_021069.1; NM_019067.1; NM_017925.1; NM_017932.1; NM_030757.1; NM_030972.1; AF126181.1; U93240.1; U90552.1; AF151056.1; U85430.1; U51007.1; BC005969.1; NM_002271.1; AL566172; and AB014576.1.

In one embodiment, the invention provides a method of diagnosing lung disease comprising the steps of measuring the expression profile of a gene group in an individual suspected of being affected or being at high risk of a lung disease (i.e. test individual), and comparing the expression profile (i.e. control profile) to an expression profile of an individual without the lung disease who has also been exposed to similar air pollutant than the test individual (i.e. control individual), wherein differences in the expression of genes when compared between the afore mentioned test individual and control individual of at least 10, more preferably at least 20, still more preferably at least 30, still more preferably at least 36, still more preferably between 36-180, still more preferably between 36-96, still more preferably between 36-84, still more preferably between 36-50, is indicative of the test individual being affected with a lung disease. Groups of about 36 genes as shown in table 4, about 50 genes as shown in table 3, about 84 genes as shown in table 2 and about 96 genes as shown in table 1 are preferred. The different gene groups can also be combined, so that the test individual can be screened for all, three, two, or just one group as shown in tables 1-4.

For example, if the expression profile of a test individual exposed to cigarette smoke is compared to the expression profile of the 50 genes shown in table 3, using the Affymetrix inc probe set on a gene chip as shown in table 3, the expression profile that is similar to the one shown in FIG. 10 for the individuals with cancer, is indicative that the test individual has cancer. Alternatively, if the expression profile is more like the expression profile of the individuals who do not have cancer in FIG. 10, the test individual likely is not affected with lung cancer.

The group of 50 genes was identified using the GenePattern server from the Broad Institute, which includes the Weighted Voting algorithm. The default settings, i.e., the signal to noise ratio and no gene filtering, were used. GenePattern is available through the World Wide Web at location broad.mit.edu/cancer/software/genepattern. This program allows analysis of data in groups rather than as individual genes. Thus, in one preferred embodiment, the expression of substantially all 50 genes of Table 3, are analyzed together. The expression profile of lower that normal expression of genes selected from the group consisting of BF218804; AK022494.1; AA114843; BE467941; NM_003541.1; R83000; AL161952.1; AK023843.1; AK021571.1; AK023783.1; AU147182; AL080112.1; AW971983; AI683552; NM_024006.1; AK026565.1; NM_014182.1; NM_021800.1; NM_016049.1; NM_019023.1; NM_021971.1; NM_014128.1; AK025651.1; AA133341; and AF198444.2, and the gene expression profile of higher than normal expression of genes selected from the group consisting of NM_007062.1; NM_001281.1; BC000120.1; NM_014255.1; BC002642.1; NM_000346.1; NM_006545.1; BG034328; NM_21822.1; NM_021069.1; NM_019067.1; NM_017925.1; NM_017932.1; NM_030757.1; NM_030972.1; AF126181.1; U93240.1; U90552.1; AF151456.1; U85430.1; U51007.1; BC005969.1; NM_002271.1; AL566172; and AB014576.1, is indicative of the individual having or being at high risk of developing lung disease, such as lung cancer. In one preferred embodiment, the expression pattern of all the genes in the Table 3 is analyzed. In one embodiment, in addition to analyzing the group of predictor genes of Table 3, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10-15, 15-20, 20-30, or more of the individual predictor genes identified using the t-test analysis are analyzed. Any combination of, for example, 5-10 or more of the group predictor genes and 5-10, or more of the individual genes can also be used.

The term “expression profile” as used herein, refers to the amount of the gene product of each of the analyzed individual genes in the sample. The “expression profile” is like a signature expression map, like the one shown for each individual in FIG. 10, on the Y-axis.

The term “lung disease”, as used herein, refers to disorders including, but not limited to, asthma, chronic bronchitis, emphysema, bronchietasis, primary pulmonary hypertension and acute respiratory distress syndrome. The methods described herein may also be used to diagnose or treat lung disorders that involve the immune system including, hypersensitivity pneumonitis, eosinophilic pneumonias, and persistent fungal infections, pulmonary fibrosis, systemic sclerosis, idiopathic pulmonary hemosiderosis, pulmonary alveolar proteinosis, cancers of the lung such as adenocarcinoma, squamous cell carcinoma, small cell and large cell carcinomas, and benign neoplasm of the lung including bronchial adenomas and hamartomas. In one preferred embodiment, the lung disease is lung cancer.

The biological samples useful according to the present invention include, but are not limited to tissue samples, cell samples, and excretion samples, such as sputum or saliva, of the airways. The samples useful for the analysis methods according to the present invention can be taken from the mouth, the bronchial airways, and the lungs.

The term “air pollutants”, as used herein, refers to any air impurities or environmental airway stress inducing agents, such as cigarette smoke, cigar smoke, smog, asbestos, and other air pollutants that have suspected or proven association to lung diseases.

The term “individual”, as used herein, preferably refers to human. However, the methods are not limited to humans, and a skilled artisan can use the diagnostic/prognostic gene groupings of the present invention in, for example, laboratory test animals, preferably animals that have lungs, such as non-human primates, murine species, including, but not limited to rats and mice, dogs, sheep, pig, guinea pigs, and other model animals.

The phrase “altered expression” as used herein, refers to either increased or decreased expression in an individual exposed to air pollutant, such as a smoker, with cancer when compared to an expression pattern of the lung cells from an individual exposed to similar air pollutant, such as smoker, who does not have cancer. Tables 1 and 2 show the preferred expression pattern changes of the invention. The terms “up” and “down” in the tables refer to the amount of expression in a smoker with cancer to the amount of expression in a smoker without cancer. Similar expression pattern changes are likely associated with development of cancer in individuals who have been exposed to other airway pollutants.

In one embodiment, the group of genes the expression of which is analyzed in diagnosis and/or prognosis of lung cancer are selected from the group of 80 genes as shown in Table 5. Any combination of genes can be selected from the 80 genes. In one embodiment, the combination of 20 genes shown in Table 7 is selected. In one embodiment, a combination of genes from Table 6 is selected.

TABLE 5 Group of 80 genes or prognostic and diagnostic testing of lung cancer. Affymetrix probe ID No. that can be Number of runs Signal to noise in a cancer sample. used to identify the gene is indicated Negative values indicate increase the gene/nucleic in cancer samples as of expression in lung cancer, acid sequence in differentially expressed positive values indicate decrease the next column Gene symbol out of 1000 test runs of expression in lung cancer. 200729_s_at ACTR2 736 −0.22284 200760_s_at ARL6IP5 483 −0.21221 201399_s_at TRAM1 611 −0.21328 201444_s_at ATP6AP2 527 −0.21487 201635_s_at FXR1 458 −0.2162 201689_s_at TPD52 565 −0.22292 201925_s_at DAF 717 −0.25875 201926_s_at DAF 591 −0.23228 201946_s_at CCT2 954 −0.24592 202118_s_at CPNE3 334 −0.21273 202704_at TOB1 943 −0.25724 202833_s_at SERPINA1 576 −0.20583 202935_s_at SOX9 750 −0.25574 203413_at NELL2 629 −0.23576 203881_s_at DMD 850 −0.24341 203908_at SLC4A4 887 −0.23167 204006_s_at FCGR3A /// FCGR3B 207 −0.20071 204403_x_at KIAA0738 923 0.167772 204427_s_at RNP24 725 −0.2366 206056_x_at SPN 976 0.196398 206169_x_at RoXaN 984 0.259637 207730_x_at HDGF2 969 0.169108 207756_at — 855 0.161708 207791_s_at RAB1A 823 −0.21704 207953_at AD7C-NTP 1000 0.218433 208137_x_at — 996 0.191938 208246_x_at TK2 982 0.179058 208654_s_at CD164 388 −0.21228 208892_s_at DUSP6 878 −0.25023 209189_at FOS 935 −0.27446 209204_at LMO4 78 0.158674 209267_s_at SLC39A8 228 −0.24231 209369_at ANXA3 384 −0.19972 209656_s_at TMEM47 456 −0.23033 209774_x_at CXCL2 404 −0.2117 210145_at PLA2G4A 475 −0.26146 210168_at C6 458 −0.24157 210317_s_at YWHAE 803 −0.29542 210397_at DEFB1 176 −0.22512 210679_x_at — 970 0.181718 211506_s_at IL8 270 −0.3105 212006_at UBXD2 802 −0.22094 213089_at LOC153561 649 0.164097 213736_at COX5B 505 0.155243 213813_x_at — 789 0.178643 214007_s_at PTK9 480 −0.21285 214146_s_at PPBP 593 −0.24265 214594_x_at ATP8B1 962 0.284039 214707_x_at ALMS1 750 0.164047 214715_x_at ZNF160 996 0.198532 215204_at SENP6 211 0.169986 215208_x_at RPL35A 999 0.228485 215385_at FTO 164 0.187634 215600_x_at FBXW12 960 0.17329 215604_x_at UBE2D2 998 0.224878 215609_at STARD7 940 0.191953 215628_x_at PPP2CA 829 0.16391 215800_at DUOX1 412 0.160036 215907_at BACH2 987 0.178338 215978_x_at LOC152719 645 0.163399 216834_at — 633 −0.25508 216858_x_at — 997 0.232969 217446_x_at — 942 0.182612 217653_x_at — 976 0.270552 217679_x_at — 987 0.265918 217715_x_at ZNF354A 995 0.223881 217826_s_at UBE2J1 812 −0.23003 218155_x_at FLJ10534 998 0.186425 218976_at DNAJC12 486 −0.22866 219392_x_at FLJ11029 867 0.169113 219678_x_at DCLRE1C 877 0.169975 220199_s_at FLJ12806 378 −0.20713 220389_at FLJ23514 102 0.239341 220720_x_at FLJ14346 989 0.17976 221191_at DKFZP434A0131 616 0.185412 221310_at FGF14 511 −0.19965 221765_at — 319 −0.25025 222027_at NUCKS 547 0.171954 222104_x_at GTF2H3 981 0.185025 222358_x_at — 564 0.194048

TABLE 6 Group of 535 genes useful in prognosis or diagnosis of lung cancer. Affymetrix probe ID No. that can be Number of runs Signal to noise in a cancer sample. used to identify the gene is indicated Negative values indicate increase the gene/nucleic in cancer samples as of expression in lung cancer, acid sequence in differentially expressed positive values indicate decrease the next column Gene symbol out of 1000 test runs of expression in lung cancer. 200729_s_at ACTR2 736 −0.22284 200760_s_at ARL6IP5 483 −0.21221 201399_s_at TRAM1 611 −0.21328 201444_s_at ATP6AP2 527 −0.21487 201635_s_at FXR1 458 −0.2162 201689_s_at TPD52 565 −0.22292 201925_s_at DAF 717 −0.25875 201926_s_at DAF 591 −0.23228 201946_s_at CCT2 954 −0.24592 202118_s_at CPNE3 334 −0.21273 202704_at TOB1 943 −0.25724 202833_s_at SERPINA1 576 −0.20583 202935_s_at SOX9 750 −0.25574 203413_at NELL2 629 −0.23576 203881_s_at DMD 850 −0.24341 203908_at SLC4A4 887 −0.23167 204006_s_at FCGR3A /// FCGR3B 207 −0.20071 204403_x_at KIAA0738 923 0.167772 204427_s_at RNP24 725 −0.2366 206056_x_at SPN 976 0.196398 206169_x_at RoXaN 984 0.259637 207730_x_at HDGF2 969 0.169108 207756_at — 855 0.161708 207791_s_at RAB1A 823 −0.21704 207953_at AD7C-NTP 1000 0.218433 208137_x_at — 996 0.191938 208246_x_at TK2 982 0.179058 208654_s_at CD164 388 −0.21228 208892_s_at DUSP6 878 −0.25023 209189_at FOS 935 −0.27446 209204_at LMO4 78 0.158674 209267_s_at SLC39A8 228 −0.24231 209369_at ANXA3 384 −0.19972 209656_s_at TMEM47 456 −0.23033 209774_x_at CXCL2 404 −0.2117 210145_at PLA2G4A 475 −0.26146 210168_at C6 458 −0.24157 210317_at YWHAE 803 −0.29542 210397_at DEFB1 176 −0.22512 210679_x_at — 970 0.181718 211506_s_at IL8 270 −0.3105 212006_at UBXD2 802 −0.22094 213089_at LOC153561 649 0.164097 213736_at COX5B 505 0.155243 213813_x_at — 789 0.178643 214007_s_at PTK9 480 −0.21285 214146_s_at PPBP 593 −0.24265 214594_x_at ATP8B1 962 0.284039 214707_x_at ALMS1 750 0.164047 214715_x_at ZNF160 996 0.198532 215204_at SENP6 211 0.169986 215208_x_at RPL35A 999 0.228485 215385_at FTO 164 0.187634 215600_x_at FBXW12 960 0.17329 215604_x_at UBE2D2 998 0.224878 215609_at STARD7 940 0.191953 215628_x_at PPP2CA 829 0.16391 215800_at DUOX1 412 0.160036 215907_at BACH2 987 0.178338 215978_x_at LOC152719 645 0.163399 216834_at — 633 −0.25508 216858_x_at — 997 0.232969 217446_x_at — 942 0.182612 217653_x_at — 976 0.270552 217679_x_at — 987 0.265918 217715_x_at ZNF354A 995 0.223881 217826_s_at UBE2J1 812 −0.23003 218155_x_at FLJ10534 998 0.186425 218976_at DNAJC12 486 −0.22866 219392_x_at FLJ11029 867 0.169113 219678_x_at DCLRE1C 877 0.169975 220199_s_at FLJ12806 378 −0.20713 220389_at FLJ23514 102 0.239341 220720_x_at FLJ14346 989 0.17976 221191_at DKFZP434A0131 616 0.185412 221310_at FGF14 511 −0.19965 221765_at — 319 −0.25025 222027_at NUCKS 547 0.171954 222104_x_at GTF2H3 981 0.186025 222358_x_at — 564 0.194048 202113_s_at SNX2 841 −0.20503 207133_x_at ALPK1 781 0.155812 218989_x_at SLC30A5 765 −0.198 200751_s_at HNRPC 759 −0.19243 220796_x_at SLC35E1 691 0.158199 209362_at SURB7 690 −0.18777 216248_s_at NR4A2 678 −0.19796 203138_at HAT1 669 −0.18115 221428_s_at TBL1XR1 665 −0.19331 218172_s_at DERL1 665 −0.16341 215861_at FLJ14031 651 0.156927 209288_s_at CDC42EP3 638 −0.20146 214001_x_at RPS10 634 0.151006 209116_x_at HBB 626 −0.12237 215595_x_at GCNT2 625 0.136319 208891_at DUSP6 617 −0.17282 215067_x_at PRDX2 616 0.160582 202918_s_at PREI3 614 −0.17003 211985_s_at CALM1 614 −0.20103 212019_at RSL1D1 601 0.152717 216187_x_at KNS2 591 0.14297 215066_at PTPRF 587 0.143323 212192_at KCTD12 581 −0.17535 217586_x_at — 577 0.147487 203582_s_at RAB4A 567 −0.18289 220113_x_at POLR1B 563 0.15764 217232_x_at HBB 561 −0.11398 201041_s_at DUSP1 560 −0.18661 211450_s_at MSH6 544 −0.15597 202648_at RPS19 533 0.150087 202936_s_at SOX9 533 −0.17714 204426_at RNP24 526 −0.18959 206392_s_at RARRES1 517 −0.18328 208750_s_at ARF1 515 −0.19797 202089_s_at SLC39A6 512 −0.19904 211297_s_at CDK7 510 −0.15992 215373_x_at FLJ12151 509 0.146742 213679_at FLJ13946 492 −0.10963 201694_s_at EGR1 490 −0.19478 209142_s_at UBE2G1 487 −0.18055 217706_at LOC220074 483 0.11787 212991_at FBXO9 476 0.148288 201289_at CYR61 465 −0.19925 206548_at FLJ23556 465 0.141583 202593_s_at MIR16 462 −0.17042 202932_at YES1 461 −0.17637 220575_at FLJ11800 461 0.116435 217713_x_at DKFZP566N034 452 0.145994 211953_s_at RANBP5 447 −0.17838 203827_at WIPI49 447 −0.17767 221997_s_at MRPL52 444 0.132649 217662_x_at BCAP29 434 0.116886 218519_at SLC35A5 428 −0.15495 214833_at KIAA0792 428 0.132943 201339_s_at SCP2 426 −0.18605 203799_at CD302 422 −0.16798 211090_s_at PRPF4B 421 −0.1838 220071_x_at C15orf25 420 0.138308 203946_s_at ARG2 415 −0.14964 213544_at ING1L 415 0.137052 209908_s_at — 414 0.131346 201688_s_at TPD52 410 −0.18965 215587_x_at BTBD14B 410 0.139952 201699_at PSMC6 409 −0.13784 214902_x_at FLJ42393 409 0.140198 214041_x_at RPL37A 402 0.106746 203987_at FZD6 392 −0.19252 211696_x_at HBB 392 −0.09508 218025_s_at PECI 389 −0.18002 215852_x_at KIAA0889 382 0.12243 209458_x_at HBA1 /// HBA2 380 −0.09796 219410_at TMEM45A 379 −0.22387 215375_x_at — 379 0.148377 206302_s_at NUDT4 376 −0.18873 208783_s_at MCP 372 −0.15076 211374_x_at — 364 0.131101 220352_x_at MGC4278 364 0.152722 216609_at TXN 363 0.15162 201942_s_at CPD 363 −0.1889 202672_s_at ATF3 361 −0.12935 204959_at MNDA 359 −0.21676 211996_s_at KIAA0220 358 0.144358 222035_s_at PAPOLA 353 −0.14487 208808_s_at HMGB2 349 −0.15222 203711_s_at HIBCH 347 −0.13214 215179_x_at PGF 347 0.146279 213562_s_at SQLE 345 −0.14669 203765_at GCA 340 −0.1798 214414_x_at HBA2 336 −0.08492 217497_at ECGF1 336 0.123255 220924_s_at SLC38A2 333 −0.17315 218139_s_at C14orf108 332 −0.15021 201096_s_at ARF4 330 −0.18887 220361_at FLJ12476 325 −0.15452 202169_s_at AASDHPPT 323 −0.15787 202527_s_at SMAD4 322 −0.18399 202166_s_at PPP1R2 320 −0.16402 204634_at NEK4 319 −0.15511 215504_x_at — 319 0.145981 202388_at RGS2 315 −0.14894 215553_x_at WDR45 315 0.137586 200598_s_at TRA1 314 −0.19349 202435_s_at CYP1B1 313 0.056937 216206_x_at MAP2K7 313 0.10383 212582_at OSBPL8 313 −0.17843 216509_x_at MLLT10 312 0.123961 200908_s_at RPLP2 308 0.136645 215108_x_at TNRC9 306 −0.1439 213872_at C6orf62 302 −0.19548 214395_x_at EEF1D 302 0.128234 222156_x_at CCPG1 301 −0.14725 201426_s_at VIM 301 −0.17461 221972_s_at Cab45 299 −0.1511 219957_at — 298 0.130796 215123_at — 295 0.125434 212515_s_at DDX3X 295 −0.14634 203357_s_at CAPN7 295 −0.17109 211711_s_at PTEN 295 −0.12636 206165_s_at CLCA2 293 −0.17699 213959_s_at KIAA1005 289 −0.16592 215083_at PSPC1 289 0.147348 219630_at PDZK1IP1 287 −0.15086 204018_x_at HBA1 /// HBA2 286 −0.08689 208671_at TDE2 286 −0.17839 203427_at ASF1A 286 −0.14737 215281_x_at POGZ 286 0.142825 205749_at CYP1A1 285 0.107118 212585_at OSBPL8 282 −0.13924 211745_x_at HBA1 /// HBA2 281 −0.08437 208078_s_at SNF1LK 278 −0.14395 218041_x_at SLC38A2 276 −0.17003 212588_at PTPRC 270 −0.1725 212397_at RDX 270 −0.15613 208268_at ADAM28 269 0.114996 207194_s_at ICAM4 269 0.127304 222252_x_at — 269 0.132241 217414_x_at HBA2 266 −0.08974 207078_at MED6 261 0.1232 215268_at KIAA0754 261 0.13669 221387_at GPR147 261 0.128737 201337_s_at VAMP3 259 −0.17284 220218_at C9orf68 259 0.125851 222356_at TBL1Y 259 0.126765 208579_x_at H2BFS 258 −0.16608 219161_s_at CKLF 257 −0.12288 202917_s_at S100A8 256 −0.19869 204455_at DST 255 −0.13072 211672_s_at ARPC4 254 −0.17791 201132_at HNRPH2 254 −0.12817 218313_s_at GALNT7 253 −0.179 218930_s_at FLJ11273 251 −0.15878 219166_at C14orf104 250 −0.14237 212805_at KIAA0367 248 −0.16649 201551_s_at LAMP1 247 −0.18035 202599_s_at NRIP1 247 −0.16226 203403_s_at RNF6 247 −0.14976 214261_s_at ADH6 242 −0.1414 202033_s_at RB1CC1 240 −0.18105 203896_s_at PLCB4 237 −0.20318 209703_x_at DKFZP586A0522 234 0.140153 211699_x_at HBA1 /// HBA2 232 −0.08369 210764_s_at CYR61 231 −0.13139 206391_at RARRES1 230 −0.16931 201312_s_at SH3BGRL 225 −0.12265 200798_x_at MCL1 221 −0.13113 214912_at — 221 0.116262 204621_s_at NR4A2 217 −0.10896 217761_at MTCBP-1 217 −0.17558 205830_at CLGN 216 −0.14737 218438_s_at MED28 214 −0.14649 207475_at FABP2 214 0.097003 208621_s_at VIL2 213 −0.19678 202436_s_at CYP1B1 212 0.042216 202539_s_at HMGCR 210 −0.15429 210830_s_at PON2 209 −0.17184 211906_s_at SERPINB4 207 −0.14728 202241_at TRIB1 207 −0.10706 203594_at RTCD1 207 −0.13823 215863_at TFR2 207 0.095157 221992_at LOC283970 206 0.126744 221872_at RARRES1 205 −0.11496 219564_at KCNJ16 205 −0.13908 201329_s_at ETS2 205 −0.14994 214188_at HIS1 203 0.1257 201667_at GJA1 199 −0.13848 201464_x_at JUN 199 −0.09858 215409_at LOC254531 197 0.094182 202583_s_at RANBP9 197 −0.13902 215594_at — 197 0.101007 214326_x_at JUND 196 −0.1702 217140_s_at VDAC1 196 −0.14682 215599_at SMA4 195 0.133438 209896_s_at PTPN11 195 −0.16258 204846_at CP 195 −0.14378 222303_at — 193 −0.10841 218218_at DIP13B 193 −0.12136 211015_s_at HSPA4 192 −0.13489 208666_s_at ST13 191 −0.13361 203191_at ABCB6 190 0.096808 202731_at PDCD4 190 −0.1545 209027_s_at ABI1 190 −0.15472 205979_at SCGB2A1 189 −0.15091 216351_x_at DAZ1 /// DAZ3 /// DAZ2 /// DAZ4 189 0.106368 220240_s_at C13orf11 188 −0.16959 204482_at CLDN5 187 0.094134 217234_s_at VIL2 186 −0.16035 214350_at SNTB2 186 0.095723 201693_s_at EGR1 184 −0.10732 212328_at KIAA1102 182 −0.12113 220168_at CASC1 181 −0.1105 203628_at IGF1R 180 0.067575 204622_x_at NR4A2 180 −0.11482 213246_at C14orf109 180 −0.16143 218728_s_at HSPC163 180 −0.13248 214753_at PFAAP5 179 0.130184 206336_at CXCL6 178 −0.05634 201445_at CNN3 178 −0.12375 209886_s_at SMAD6 176 0.079296 213376_at ZBTB1 176 −0.17777 213887_s_at POLR2E 175 −0.16392 204783_at MLF1 174 −0.13409 218824_at FLJ10781 173 0.1394 212417_at SCAMP1 173 −0.17052 202437_s_at CYP1B1 171 0.033438 217528_at CLCA2 169 −0.14179 218170_at ISOC1 169 −0.14064 206278_at PTAFR 167 0.087096 201939_at PLK2 167 −0.11049 200907_s_at KIAA0992 166 −0.18323 207480_s_at MEIS2 166 −0.15232 201417_at SOX4 162 −0.09617 213826_s_at — 160 0.097313 214953_s_at APP 159 −0.1645 204897_at PTGER4 159 −0.08152 201711_x_at RANBP2 158 −0.17192 202457_s_at PPP3CA 158 −0.18821 206683_at ZNF165 158 −0.08848 214581_x_at TNFRSF21 156 −0.14624 203392_s_at CTBP1 155 −0.16161 212720_at PAPOLA 155 −0.14809 207758_at PPM1F 155 0.090007 220995_at STXBP6 155 0.106749 213831_at HLA-DQA1 154 0.193368 212044_s_at — 153 0.098889 202434_s_at CYP1B1 153 0.049744 206166_s_at CLCA2 153 −0.1343 218343_s_at GTF3C3 153 −0.13066 202557_at STCH 152 −0.14894 201133_s_at PJA2 152 −0.18481 213605_s_at MGC22265 151 0.130895 210947_s_at MSH3 151 −0.12595 208310_s_at C7orf28A /// C7orf28B 151 −0.15523 209307_at — 150 −0.1667 215387_x_at GPC6 148 0.114691 213705_at MAT2A 147 0.104855 213979_s_at — 146 0.121562 212731_at LOC157567 146 −0.1214 210117_at SPAG1 146 −0.11236 200641_s_at YWHAZ 145 −0.14071 210701_at CFDP1 145 0.151664 217152_at NCOR1 145 0.130891 204224_s_at GCH1 144 −0.14574 202028_s_at — 144 0.094276 201735_s_at CLCN3 144 −0.1434 208447_s_at PRPS1 143 −0.14933 220926_s_at C1orf22 142 −0.17477 211505_s_at STAU 142 −0.11618 221684_s_at NYX 142 0.102298 206906_at ICAM5 141 0.076813 213228_at PDE8B 140 −0.13728 217202_s_at GLUL 139 −0.15489 211713_x_at KIAA0101 138 0.108672 215012_at ZNF451 138 0.13269 200806_s_at HSPD1 137 −0.14811 201466_s_at JUN 135 −0.0667 211564_s_at PDLIM4 134 −0.12756 207850_at CXCL3 133 −0.17973 221841_s_at KLF4 133 −0.1415 200605_s_at PRKAR1A 132 −0.15642 221198_at SCT 132 0.08221 201772_at AZIN1 131 −0.16639 205009_at TFF1 130 −0.17578 205542_at STEAP1 129 −0.08498 218195_at C6orf211 129 −0.14497 213642_at — 128 0.079657 212891_s_at GADD45GIP1 128 −0.09272 202798_at SEC24B 127 −0.12621 222207_x_at — 127 0.10783 202638_s_at ICAM1 126 0.070364 200730_s_at PTP4A1 126 −0.15289 219355_at FLJ10178 126 −0.13407 220266_s_at KLF4 126 −0.15324 201259_s_at SYPL 124 −0.16643 209649_at STAM2 124 −0.1696 220094_s_at C6orf79 123 −0.12214 221751_at PANK3 123 −0.1723 200008_s_at GDI2 123 −0.15852 205078_at PIGF 121 −0.13747 218842_at FLJ21908 121 −0.08903 202536_at CHMP2B 121 −0.14745 220184_at NANOG 119 0.098142 201117_s_at CPE 118 −0.20025 219787_s_at ECT2 117 −0.14278 206628_at SLC5A1 117 −0.12838 204007_at FCGR3B 116 −0.15337 209446_s_at — 116 0.100508 211612_s_at IL13RA1 115 −0.17266 220992_s_at C1orf25 115 −0.11026 221899_at PFAAP5 115 0.11698 221719_s_at LZTS1 115 0.093494 201473_at JUNB 114 −0.10249 221193_s_at ZCCHC10 112 −0.08003 215659_at GSDML 112 0.118288 205157_s_at KRT17 111 −0.14232 201001_s_at UBE2V1 /// Kua-UEV 111 −0.16786 216789_at — 111 0.105386 205506_at VIL1 111 0.097452 204875_s_at GMDS 110 −0.12995 207191_s_at ISLR 110 0.100627 202779_s_at UBE2S 109 −0.11364 210370_s_at LY9 109 0.096323 202842_s_at DNAJB9 108 −0.15326 201082_s_at DCTN1 107 −0.10104 215588_x_at RIOK3 107 0.135837 211076_x_at DRPLA 107 0.102743 210230_at — 106 0.115001 206544_x_at SMARCA2 106 −0.12099 208852_s_at CANX 105 −0.14776 215405_at MYO1E 105 0.086393 208653_s_at CD164 104 −0.09185 206355_at GNAL 103 0.1027 210793_s_at NUP98 103 −0.13244 215070_x_at RABGAP1 103 0.125029 203007_x_at LYPLA1 102 −0.17961 203841_x_at MAPRE3 102 −0.13389 206759_at FCER2 102 0.081733 202232_s_at GA17 102 −0.11373 215892_at — 102 0.13866 214359_s_at HSPCB 101 −0.12276 215810_x_at DST 101 0.098963 208937_s_at ID1 100 −0.06552 213664_at SLC1A1 100 −0.12654 219338_s_at FLJ20156 100 −0.10332 206595_at CST6 99 −0.10059 207300_s_at F7 99 0.082445 213792_s_at INSR 98 0.137962 209674_at CRY1 98 −0.13818 40665_at FMO3 97 −0.05976 217975_at WBP5 97 −0.12698 210296_s_at PXMP3 97 −0.13537 215483_at AKAP9 95 0.125966 212633_at KIAA0776 95 −0.16778 206164_at CLCA2 94 −0.13117 216813_at — 94 0.089023 208925_at C3orf4 94 −0.1721 219469_at DNCH2 94 −0.12003 206016_at CXorf37 93 −0.11569 216745_x_at LRCH1 93 0.117149 212999_x_at HLA-DQB1 92 0.110258 216859_x_at — 92 0.116351 201636_at — 92 −0.13501 204272_at LGALS4 92 0.110391 215454_x_at SFTPC 91 0.064918 215972_at — 91 0.097654 220593_s_at FLJ20753 91 0.095702 222009_at CGI-14 91 0.070949 207115_x_at MBTD1 91 0.107883 216922_x_at DAZ1 /// DAZ3 /// DAZ2 /// DAZ4 91 0.086888 217626_at AKR1C1 /// AKR1C2 90 0.036545 211429_s_at SERPINA1 90 −0.11406 209662_at CETN3 90 −0.10879 201629_s_at ACP1 90 −0.14441 201236_s_at BTG2 89 −0.09435 217137_x_at — 89 0.070954 212476_at CENTB2 89 −0.1077 218545_at FLJ11088 89 −0.12452 208857_s_at PCMT1 89 −0.14704 221931_s_at SEH1L 88 −0.11491 215046_at FLJ23861 88 −0.14667 220222_at PRO1905 88 0.081524 209737_at AIP1 87 −0.07696 203949_at MPO 87 0.113273 219290_x_at DAPP1 87 0.111366 205116_at LAMA2 86 0.05845 222316_at VDP 86 0.091505 203574_at NFIL3 86 −0.14335 207820_at ADH1A 86 0.104444 203751_x_at JUND 85 −0.14118 202930_s_at SUCLA2 85 −0.14884 215404_x_at FGFR1 85 0.119684 216266_s_at ARFGEF1 85 −0.12432 212806_at KIAA0367 85 −0.13259 219253_at — 83 −0.14094 214605_x_at GPR1 83 0.114443 205403_at IL1R2 82 −0.19721 222282_at PAPD4 82 0.128004 214129_at PDE4DIP 82 −0.13913 209259_s_at CSPG6 82 −0.12618 216900_s_at CHRNA4 82 0.105518 221943_x_at RPL38 80 0.086719 215386_at AUTS2 80 0.129921 201990_s_at CREBL2 80 −0.13645 220145_at FLJ21159 79 −0.16097 221173_at USH1C 79 0.109348 214900_at ZKSCAN1 79 0.075517 203290_at HLA-DQA1 78 −0.20756 215382_x_at TPSAB1 78 −0.09041 201631_s_at IER3 78 −0.12038 212188_at KCTD12 77 −0.14672 220428_at CD207 77 0.101238 215349_at — 77 0.10172 213928_s_at HRB 77 0.092136 221228_s_at — 77 0.0859 202069_s_at IDH3A 76 −0.14747 208554_at POU4F3 76 0.107529 209504_s_at PLEKHB1 76 −0.13125 212989_at TMEM23 75 −0.11012 216197_at ATF7IP 75 0.115016 204748_at PTGS2 74 −0.15194 205221_at HGD 74 0.096171 214705_at INADL 74 0.102919 213939_s_at RIPX 74 0.091175 203691_at PI3 73 −0.14375 220532_s_at LR8 73 −0.11682 209829_at C6orf32 73 −0.08982 206515_at CYP4F3 72 0.104171 218541_s_at C8orf4 72 −0.09551 210732_s_at LGALS8 72 −0.13683 202643_s_at TNFAIP3 72 −0.16699 218963_s_at KRT23 72 −0.10915 213304_at KIAA0423 72 −0.12256 202768_at FOSB 71 −0.06289 205623_at ALDH3A1 71 0.045457 206488_s_at CD36 71 −0.15899 204319_s_at RGS10 71 −0.10107 217811_at SELT 71 −0.16162 202746_at ITM2A 70 −0.06424 221127_s_at RIG 70 0.110593 209821_at C9orf26 70 −0.07383 220957_at CTAGE1 70 0.092986 215577_at UBE2E1 70 0.10305 214731_at DKFZp547A023 70 0.102821 210512_s_at VEGF 69 −0.11804 205267_at POU2AF1 69 0.101353 216202_s_at SPTLC2 69 −0.11908 220477_s_at C20orf30 69 −0.16221 205863_at S100A12 68 −0.10353 215780_s_at SET /// LOC389168 68 −0.10381 218197_s_at OXR1 68 −0.14424 203077_s_at SMAD2 68 −0.11242 222339_x_at — 68 0.121585 200698_at KDELR2 68 −0.15907 210540_s_at B4GALT4 67 −0.13556 217725_x_at PAI-RBP1 67 −0.14956 217082_at — 67 0.086098

TABLE 7 Group of 20 genes useful in prognosis and/or diagnosis of lung cancer. Signal to noise in a cancer sample. Negative values Number of runs indicate increase Affymetrix probe the gene is of expression ID No. that can be indicated in in lung cancer, used to identify cancer samples positive values the gene/nucleic as differentially indicate decrease acid sequence in Gene expressed out of of expression the next column symbol 1000 test runs in lung cancer. 207953_at AD7C-NTP 1000 0.218433 215208_x_at RPL35A  999 0.228485 215604_x_at UBE2D2  998 0.224878 218155_x_at FLJ10534  998 0.186425 216858_x_at —  997 0.232969 208137_x_at —  996 0.191938 214715_x_at ZNF160  996 0.198532 217715_x_at ZNF354A  995 0.223881 220720_x_at FLJ14346  989 0.17976  215907_at BACH2  987 0.178338 217679_x_at —  987 0.265918 206169_x_at RoXaN  984 0.259637 208246_x_at TK2  982 0.179058 222104_x_at GTF2H3  981 0.186625 206056_x_at SPN  976 0.196398 217653_x_at —  976 0.270552 210679_x_at —  970 0.481718 207730_x_at HDGF2  969 0.169108 214594_x_at ATP8B1  962 0.284039

One can use the above tables to correlate or compare the expression of the transcript to the expression of the gene product. Increased expression of the transcript as shown in the table corresponds to increased expression of the gene product. Similarly, decreased expression of the transcript as shown in the table corresponds to decreased expression of the gene product

The analysis of the gene expression of one or more genes and/or transcripts of the groups or their subgroups of the present invention can be performed using any gene expression method known to one skilled in the art. Such methods include, but are not limited to expression analysis using nucleic acid chips (e.g. Affymetrix chips) and quantitative RT-PCR based methods using, for example real-time detection of the transcripts. Analysis of transcript levels according to the present invention can be made using total or messenger RNA or proteins encoded by the genes identified in the diagnostic gene groups of the present invention as a starting material. In the preferred embodiment the analysis is an immunohistochemical analysis with an antibody directed against proteins comprising at least about 10-20, 20-30, preferably at least 36, at least 36-50, 50, about 50-60, 60-70, 70-80, 80-90, 96, 100-180, 180-200, 200-250, 250-300, 300-350, 350-400, 400-450, 450-500, 500-535 proteins encoded by the genes and/or transcripts as shown in Tables 1-7.

The methods of analyzing transcript levels of the gene groups in an individual include Northern-blot hybridization, ribonuclease protection assay, and reverse transcriptase polymerase chain reaction (RT-PCR) based methods. The different RT-PCR based techniques are the most suitable quantification method for diagnostic purposes of the present invention, because they are very sensitive and thus require only a small sample size which is desirable for a diagnostic test. A number of quantitative RT-PCR based methods have been described and are useful in measuring the amount of transcripts according to the present invention. These methods include RNA quantification using PCR and complementary DNA (cDNA) arrays (Shalon et al., Genome Research 6(7):639-45, 1996; Bernard et al., Nucleic Acids Research 24(8): 1435-42, 1996), real competitive PCR using a MALDI-TOF Mass spectrometry based approach (Ding et al, PNAS, 100: 3059-64, 2003), solid-phase mini-sequencing technique, which is based upon a primer extension reaction (U.S. Pat. No. 6,013,431, Suomalainen et al. Mol. Biotechnol. June; 15(2): 123-31, 2000), ion-pair high-performance liquid chromatography (Doris et al. J. Chromatogr. A May 8; 806(1):47-60, 1998), and 5′ nuclease assay or real-time RT-PCR (Holland et al. Proc Natl Acad Sci USA 88: 7276-7280, 1991).

Methods using RT-PCR and internal standards differing by length or restriction endonuclease site from the desired target sequence allowing comparison of the standard with the target using gel electrophoretic separation methods followed by densitometric quantification of the target have also been developed and can be used to detect the amount of the transcripts according to the present invention (see, e.g., U.S. Pat. Nos. 5,876,978; 5,643,765; and 5,639,606.

The samples are preferably obtained from bronchial airways using, for example, endoscopic cytobrush in connection with a fiber optic bronchoscopy. In one embodiment, the cells are obtained from the individual's mouth buccal cells, using, for example, a scraping of the buccal mucosa.

In one preferred embodiment, the invention provides a prognostic and/or diagnostic immunohistochemical approach, such as a dip-stick analysis, to determine risk of developing lung disease. Antibodies against proteins, or antigenic epitopes thereof, that are encoded by the group of genes of the present invention, are either commercially available or can be produced using methods well know to one skilled in the art.

The invention contemplates either one dipstick capable of detecting all the diagnostically important gene products or alternatively, a series of dipsticks capable of detecting the amount proteins of a smaller sub-group of diagnostic proteins of the present invention.

Antibodies can be prepared by means well known in the art. The term “antibodies” is meant to include monoclonal antibodies, polyclonal antibodies and antibodies prepared by recombinant nucleic acid techniques that are selectively reactive with a desired antigen. Antibodies against the proteins encoded by any of the genes in the diagnostic gene groups of the present invention are either known or can be easily produced using the methods well known in the art. Internet sites such as Biocompare through the World Wide Web at “biocompare.com/abmatrix.asp?antibody=y” provide a useful tool to anyone skilled in the art to locate existing antibodies against any of the proteins provided according to the present invention.

Antibodies against the diagnostic proteins according to the present invention can be used in standard techniques such as Western blotting or immunohistochemistry to quantify the level of expression of the proteins of the diagnostic airway proteome. This is quantified according to the expression of the gene transcript, i.e. the increased expression of transcript corresponds to increased expression of the gene product, i.e. protein. Similarly decreased expression of the transcript corresponds to decreased expression of the gene product or protein. Detailed guidance of the increase or decrease of expression of preferred transcripts in lung disease, particularly lung cancer, is set forth in the tables. For example, Tables 5 and 6 describe a group of genes the expression of which is altered in lung cancer.

Immunohistochemical applications include assays, wherein increased presence of the protein can be assessed, for example, from a saliva or sputum sample.

The immunohistochemical assays according to the present invention can be performed using methods utilizing solid supports. The solid support can be a any phase used in performing immunoassays, including dipsticks, membranes, absorptive pads, beads, microtiter wells, test tubes, and the like. Preferred are test devices which may be conveniently used by the testing personnel or the patient for self-testing, having minimal or no previous training. Such preferred test devices include dipsticks, membrane assay systems as described in U.S. Pat. No. 4,632,901. The preparation and use of such conventional test systems is well described in the patent, medical, and scientific literature. If a stick is used, the anti-protein antibody is bound to one end of the stick such that the end with the antibody can be dipped into the solutions as described below for the detection of the protein. Alternatively, the samples can be applied onto the antibody-coated dipstick or membrane by pipette or dropper or the like.

The antibody against proteins encoded by the diagnostic airway transcriptome (the “protein”) can be of any isotype, such as IgA, IgG or IgM, Fab fragments, or the like. The antibody may be a monoclonal or polyclonal and produced by methods as generally described, for example, in Harlow and Lane, Antibodies, A Laboratory Manual, Cold Spring Harbor Laboratory, 1988, incorporated herein by reference. The antibody can be applied to the solid support by direct or indirect means. Indirect bonding allows maximum exposure of the protein binding sites to the assay solutions since the sites are not themselves used for binding to the support. Preferably, polyclonal antibodies are used since polyclonal antibodies can recognize different epitopes of the protein thereby enhancing the sensitivity of the assay.

The solid support is preferably non-specifically blocked after binding the protein antibodies to the solid support. Non-specific blocking of surrounding areas can be with whole or derivatized bovine serum albumin, or albumin from other animals, whole animal serum, casein, non-fat milk, and the like.

The sample is applied onto the solid support with bound protein-specific antibody such that the protein will be bound to the solid support through said antibodies. Excess and unbound components of the sample are removed and the solid support is preferably washed so the antibody-antigen complexes are retained on the solid support. The solid support may be washed with a washing solution which may contain a detergent such as Tween-20, Tween-80 or sodium dodecyl sulfate.

After the protein has been allowed to bind to the solid support, a second antibody which reacts with protein is applied. The second antibody may be labeled, preferably with a visible label. The labels may be soluble or particulate and may include dyed immunoglobulin binding substances, simple dyes or dye polymers, dyed latex beads, dye-containing liposomes, dyed cells or organisms, or metallic, organic, inorganic, or dye solids. The labels may be bound to the protein antibodies by a variety of means that are well known in the art. In some embodiments of the present invention, the labels may be enzymes that can be coupled to a signal producing system. Examples of visible labels include alkaline phosphatase, beta-galactosidase, horseradish peroxidase, and biotin. Many enzyme-chromogen or enzyme-substrate-chromogen combinations are known and used for enzyme-linked assays. Dye labels also encompass radioactive labels and fluorescent dyes.

Simultaneously with the sample, corresponding steps may be carried out with a known amount or amounts of the protein and such a step can be the standard for the assay. A sample from a healthy individual exposed to a similar air pollutant such as cigarette smoke, can be used to create a standard for any and all of the diagnostic gene group encoded proteins.

The solid support is washed again to remove unbound labeled antibody and the labeled antibody is visualized and quantified. The accumulation of label will generally be assessed visually. This visual detection may allow for detection of different colors, for example, red color, yellow color, brown color, or green color, depending on label used. Accumulated label may also be detected by optical detection devices such as reflectance analyzers, video image analyzers and the like. The visible intensity of accumulated label could correlate with the concentration of protein in the sample. The correlation between the visible intensity of accumulated label and the amount of the protein may be made by comparison of the visible intensity to a set of reference standards. Preferably, the standards have been assayed in the same way as the unknown sample, and more preferably alongside the sample, either on the same or on a different solid support.

The concentration of standards to be used can range from about 1 mg of protein per liter of solution, up to about 50 mg of protein per liter of solution. Preferably, two or more different concentrations of an airway gene group encoded proteins are used so that quantification of the unknown by comparison of intensity of color is more accurate.

For example, the present invention provides a method for detecting risk of developing lung cancer in a subject exposed to cigarette smoke comprising measuring the transcription profile of the proteins encoded by one or more groups of genes of the invention in a biological sample of the subject. Preferably at least about 30, still more preferably at least about 36, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, or about 180 of the proteins encoded by the airway transcriptome in a biological sample of the subject are analyzed. The method comprises binding an antibody against each protein encoded by the gene in the gene group (the “protein”) to a solid support chosen from the group consisting of dip-stick and membrane; incubating the solid support in the presence of the sample to be analyzed under conditions where antibody-antigen complexes form; incubating the support with an anti-protein antibody conjugated to a detectable moiety which produces a signal; visually detecting said signal, wherein said signal is proportional to the amount of protein in said sample; and comparing the signal in said sample to a standard, wherein a difference in the amount of the protein in the sample compared to said standard of the same group of proteins, is indicative of diagnosis of or an increased risk of developing lung cancer. The standard levels are measured to indicate expression levels in an airway exposed to cigarette smoke where no cancer has been detected.

The assay reagents, pipettes/dropper, and test tubes may be provided in the form of a kit. Accordingly, the invention further provides a test kit for visual detection of the proteins encoded by the airway gene groups, wherein detection of a level that differs from a pattern in a control individual is considered indicative of an increased risk of developing lung disease in the subject. The test kit comprises one or more solutions containing a known concentration of one or more proteins encoded by the airway transcriptome (the “protein”) to serve as a standard; a solution of a anti-protein antibody bound to an enzyme; a chromogen which changes color or shade by the action of the enzyme; a solid support chosen from the group consisting of dip-stick and membrane carrying on the surface thereof an antibody to the protein. Instructions including the up or down regulation of the each of the genes in the groups as provided by the Tables 1 and 2 are included with the kit.

The practice of the present invention may employ, unless otherwise indicated, conventional techniques and descriptions of organic chemistry, polymer technology, molecular biology (including recombinant techniques), cell biology, biochemistry, and immunology, which are within the skill of the art. Such conventional techniques include polymer array synthesis, hybridization, ligation, and detection of hybridization using a label. Specific illustrations of suitable techniques can be had by reference to the example herein below. However, other equivalent conventional procedures can, of course, also be used. Such conventional techniques and descriptions can be found in standard laboratory manuals such as Genome Analysis: A Laboratory Manual Series (Vols. I-IV), Using Antibodies: A Laboratory Manual, Cells: A Laboratory Manual, PCR Primer: A Laboratory Manual, and Molecular Cloning: A Laboratory Manual (all from Cold Spring Harbor Laboratory Press), Stryer, L. (1995) Biochemistry (4th Ed.) Freeman, N.Y., Gait, “Oligonucleotide Synthesis: A Practical Approach” 1984, IRL Press, London, Nelson and Cox (2000), Lehninger, Principles of Biochemistry 3^(rd) Ed., W. H. Freeman Pub., New York, N.Y. and Berg et al. (2002) Biochemistry, 5^(th) Ed., W.H. Freeman Pub., New York, N.Y., all of which are herein incorporated in their entirety by reference for all purposes.

The methods of the present invention can employ solid substrates, including arrays in some preferred embodiments. Methods and techniques applicable to polymer (including protein) array synthesis have been described in U.S. Ser. No. 09/536,841, WO 00/58516, U.S. Pat. Nos. 5,143,854, 5,242,974, 5,252,743, 5,324,633, 5,384,261, 5,405,783, 5,424,186, 5,451,683, 5,482,867, 5,491,074, 5,527,681, 5,550,215, 5,571,639, 5,578,832, 5,593,839, 5,599,695, 5,624,711, 5,631,734, 5,795,716, 5,831,070, 5,837,832, 5,856,101, 5,858,659, 5,936,324, 5,968,740, 5,974,164, 5,981,185, 5,981,956, 6,025,601, 6,033,860, 6,040,193, 6,090,555, 6,136,269, 6,269,846 and 6,428,752, in PCT Applications Nos. PCT/US99/00730 (International Publication Number WO 99/36760) and PCT/US01/04285, which are all incorporated herein by reference in their entirety for all purposes.

Patents that describe synthesis techniques in specific embodiments include U.S. Pat. Nos. 5,412,087, 6,147,205, 6,262,216, 6,310,189, 5,889,165, and 5,959,098. Nucleic acid arrays are described in many of the above patents, but the same techniques are applied to polypeptide and protein arrays.

Nucleic acid arrays that are useful in the present invention include, but are not limited to those that are commercially available from Affymetrix (Santa Clara, Calif.) under the brand name GeneChip7. Example arrays are shown on the website at affymetrix.com.

Examples of gene expression monitoring, and profiling methods that are useful in the methods of the present invention are shown in U.S. Pat. Nos. 5,800,992, 6,013,449, 6,020,135, 6,033,860, 6,040,138, 6,177,248 and 6,309,822. Other examples of uses are embodied in U.S. Pat. Nos. 5,871,928, 5,902,723, 6,045,996, 5,541,061, and 6,197,506.

The present invention also contemplates sample preparation methods in certain preferred embodiments. Prior to or concurrent with expression analysis, the nucleic acid sample may be amplified by a variety of mechanisms, some of which may employ PCR. See, e.g., PCR Technology: Principles and Applications for DNA Amplification (Ed. H. A. Erlich, Freeman Press, NY, N.Y., 1992); PCR Protocols: A Guide to Methods and Applications (Eds. Innis, et al., Academic Press, San Diego, Calif., 1990); Mattila et al., Nucleic Acids Res. 19, 4967 (1991); Eckert et al., PCR Methods and Applications 1, 17 (1991); PCR (Eds. McPherson et al., IRL Press, Oxford); and U.S. Pat. Nos. 4,683,202, 4,683,195, 4,800,159 4,965,188, and 5,333,675, and each of which is incorporated herein by reference in their entireties for all purposes. The sample may be amplified on the array. See, for example, U.S. Pat. No. 6,300,070 and U.S. patent application Ser. No. 09/513,300, which are incorporated herein by reference.

Other suitable amplification methods include the ligase chain reaction (LCR) (e.g., Wu and Wallace, Genomics 4, 560 (1989), Landegren et al., Science 241, 1077 (1988) and Barringer et al. Gene 89:117 (1990)), transcription amplification (Kwoh et al., Proc. Natl. Acad. Sci. USA 86, 1173 (1989) and WO88/10315), self-sustained sequence replication (Guatelli et al., Proc. Nat. Acad Sci. USA, 87, 1874 (1990) and WO90/06995), selective amplification of target polynucleotide sequences (U.S. Pat. No. 6,410,276), consensus sequence primed polymerase chain reaction (CP-PCR) (U.S. Pat. No. 4,437,975), arbitrarily primed polymerase chain reaction (AP-PCR) (U.S. Pat. Nos. 5,413,909, 5,861,245) and nucleic acid based sequence amplification (NABSA). (U.S. Pat. Nos. 5,409,818, 5,554,517, and 6,063,603). Other amplification methods that may be used are described in, U.S. Pat. Nos. 5,242,794, 5,494,810, 4,988,617 and in U.S. Ser. No. 09/854,317, each of which is incorporated herein by reference.

Additional methods of sample preparation and techniques for reducing the complexity of a nucleic sample are described, for example, in Dong et al., Genome Research 11, 1418 (2001), in U.S. Pat. Nos. 6,361,947, 6,391,592 and U.S. patent application Ser. Nos. 09/916,135, 09/920,491, 09/910,292, and 10/013,598.

Methods for conducting polynucleotide hybridization assays have been well developed in the art. Hybridization assay procedures and conditions will vary depending on the application and are selected in accordance with the general binding methods known including those referred to in: Maniatis et al. Molecular Cloning: A Laboratory Manual (2^(nd) Ed. Cold Spring Harbor, N.Y., 1989); Berger and Kimmel Methods in Enzymology, Vol. 152, Guide to Molecular Cloning Techniques (Academic Press, Inc., San Diego, Calif., 1987); Young and Davism, P.N. A.S, 80: 1194 (1983). Methods and apparatus for carrying out repeated and controlled hybridization reactions have been described, for example, in U.S. Pat. Nos. 5,871,928, 5,874,219, 6,045,996 and 6,386,749, 6,391,623 each of which are incorporated herein by reference

The present invention also contemplates signal detection of hybridization between the sample and the probe in certain embodiments. See, for example, U.S. Pat. Nos. 5,143,854, 5,578,832; 5,631,734; 5,834,758; 5,936,324; 5,981,956; 6,025,601; 6,141,096; 6,185,030; 6,201,639; 6,218,803; and 6,225,625, in provisional U.S. patent application Ser. No. 60/364,731 and in PCT Application PCT/US99/06097 (published as WO99/47964).

Examples of methods and apparatus for signal detection and processing of intensity data are disclosed in, for example, U.S. Pat. Nos. 5,143,854, 5,547,839, 5,578,832, 5,631,734, 5,800,992, 5,834,758; 5,856,092, 5,902,723, 5,936,324, 5,981,956, 6,025,601, 6,090,555, 6,141,096, 6,185,030, 6,201,639; 6,218,803; and 6,225,625, in U.S. patent application Ser. No. 60/364,731 and in PCT Application PCT/US99/06097 (published as WO99/47964).

The practice of the present invention may also employ conventional biology methods, software and systems. Computer software products of the invention typically include computer readable medium having computer-executable instructions for performing the logic steps of the method of the invention. Suitable computer readable medium include floppy disk, CD-ROM/DVD/DVD-ROM, hard-disk drive, flash memory, ROM/RAM, magnetic tapes and etc. The computer executable instructions may be written in a suitable computer language or combination of several languages. Basic computational biology methods are described in, e.g. Setubal and Meidanis et al., Introduction to Computational Biology Methods (PWS Publishing Company, Boston, 1997); Salzberg, Searles, Kasif, (Ed.), Computational Methods in Molecular Biology, (Elsevier, Amsterdam, 1998); Rashidi and Buehler, Bioinformatics Basics: Application in Biological Science and Medicine (CRC Press, London, 2000) and Ouelette and Bzevanis Bioinformatics: A Practical Guide for Analysis of Gene and Proteins (Wiley & Sons, Inc., 2^(nd) ed., 2001).

The present invention also makes use of various computer program products and software for a variety of purposes, such as probe design, management of data, analysis, and instrument operation. See, for example, U.S. Pat. Nos. 5,593,839, 5,795,716, 5,733,729, 5,974,164, 6,066,454, 6,090,555, 6,185,561, 6,188,783, 6,223,127, 6,229,911 and 6,308,170.

Additionally, the present invention may have embodiments that include methods for providing gene expression profile information over networks such as the Internet as shown in, for example, U.S. patent application Ser. Nos. 10/063,559, 60/349,546, 60/376,003, 60/394,574, 60/403,381.

Throughout this specification, various aspects of this invention are presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the invention. Accordingly, the description of a range should be considered to have specifically disclosed all the possible sub-ranges as well as individual numerical values within that range. For example, description of a range such as from 10-20 should be considered to have specifically disclosed sub-ranges such as from 10-13, from 10-14, from 10-15, from 11-14, from 11-16, etc., as well as individual numbers within that range, for example, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, and 20. This applies regardless of the breadth of the range. In addition, the fractional ranges are also included in the exemplified amounts that are described. Therefore, for example, a range of 1-3 includes fractions such as 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, etc. This applies particularly to the amount of increase or decrease of expression of any particular gene or transcript.

The present invention has many preferred embodiments and relies on many patents, applications and other references for details known to those of the art. Therefore, when a patent, application, or other reference is cited or repeated throughout the specification, it should be understood that it is incorporated by reference in its entirety for all purposes as well as for the proposition that is recited.

EXAMPLES Example 1

In this study, we used three study groups: 1) normal non-smokers (n=23); 2) smokers without cancer (active v. former smokers) (n=52); 3) smokers with suspect cancer (n=98: 45 cancer, 53 no cancer).

We obtained epithelial nucleic acids (RNA/DNA) from epithelial cells in mouth and airway (bronchoscopy). We also obtained nucleic acids from blood to provide one control.

We analyzed gene expression using RNA and U133A Affymetrix array that represents transcripts from about 22,500 genes.

The microarray data analysis was performed as follows. We first scanned the Affymetrix chips that had been hybridized with the study group samples. The obtained microarray raw data consisted of signal strength and detection p-value. We normalized or scaled the data, and filtered the poor quality chips based on images, control probes, and histograms according to standard Affymetrix instructions. We also filtered contaminated specimens which contained non-epithelial cells. Lastly, the genes of importance were filtered using detection p-value. This resulted in identification of transcripts present in normal airways (normal airway transcriptome), with variability and multiple regression analysis. This also resulted in identification of effects of smoking on airway epithelial cell transcription. For this, we used T-test and Pearson correlation analysis. We also identified a group or a set of transcripts that were differentially expressed in samples with lung cancer and samples without cancer. This analysis was performed using class prediction models.

We used weighted voting method. The weighted voting method ranks, and gives a weight “p” to all genes by the signal to noise ration of gene expression between two classes: P=mean_((class 1))−mean_((class2))/sd_((class 1))=sd_((class 2)). Committees of variable sizes of the top ranked genes were used to evaluate test samples, but genes with more significant p-values were more heavily weighed. Each committee genes in test sample votes for one class or the other, based on how close that gene expression level is to the class 1 mean or the class 2 mean. V_((gene A))=P_((gene A)), i.e. level of expression in test sample less the average of the mean expression values in the two classes. Votes for each class were tallied and the winning class was determined along with prediction strength as PS=V_(win)−V_(lose)/V_(win)+V_(lose). Finally, the accuracy was validated using cross-validation +/− independent samples.

FIG. 8 shows diagrams of the class prediction model analysis used in the Example 1.

The results of the weighted voting method for a 50 gene group analysis (50 gene committee) were as follows. Cross-validation (n=74) resulted in accuracy of 81%, with sensitivity of 76% and specificity of 85%. In an independent dataset (n=24) the accuracy was 88%, with sensitivity of 75% and specificity of 100%.

We note that with sensitivity to bronchoscopy alone only 18/45 (40%) of cancers were diagnosed at the time of bronchoscopy using brushings, washings, biopsy or Wang.

We performed a gene expression analysis of the human genome using isolated nucleic acid samples comprising lung cell transcripts from individuals. The chip used was the Human Genome U133 Set. We used Microarray Suite 5.0 software to analyze raw data from the chip (i.e. to convert the image file into numerical data). Both the chip and the software are proprietary materials from Affymetrix. Bronchoscopy was performed to obtain nucleic acid samples from 98 smoker individuals.

We performed a Student's t-test using gene expression analysis of 45 smokers with lung cancer and 53 smokers without lung cancer. We identified several groups of genes that showed significant variation in their expression between smokers with cancer and smokers without cancer. We further identified at least three groups of genes that, when their expression was analyzed in combination, the results allowed us to significantly increase diagnostic power in identifying cancer carrying smokers from smokers without cancer.

The predictor groups of genes were identified using the GenePattern server from the Broad Institute, which includes the Weighted Voting algorithm. The default settings, i.e., the signal to noise ratio and no gene filtering, were used. GenePattern is available at World Wide Web from broad.mit.edu/cancer/software/genepattern. This program allows analysis of data in groups rather than as individual genes.

Table 1 shows the top 96 genes from our analysis with different expression patterns in smokers with cancer and smokers without cancer.

Table 2 shows the 84 genes that were also identified in our previous screens as individual predictors of lung cancer.

Table 4 shows a novel group of 36 genes the expression of which was different between the smokers with cancer and smokers without cancer.

Table 3 shows a group of 50 genes that we identified as most predictive of development of cancer in smokers. That is, that when the expression of these genes was analyzed and reflected the pattern (expression down or up) as shown in Table 3, we could identify the individuals who will develop cancer based on this combined expression profile of these genes. When used in combination, the expression analysis of these 50 genes was predictive of a smoker developing lung cancer in over 70% of the samples. Accuracy of diagnosis of lung cancer in our sample was 80-85% on cross-validation and independent dataset (accuracy includes both the sensitivity and specificity). The sensitivity (percent of cancer cases correctly diagnosed) was approximately 75% as compared to sensitivity of 40% using standard bronchoscopy technique. (Specificity is percent of non-cancer cases correctly diagnosed).

These data show the dramatic increase of diagnostic power that can be reached using the expression profiling of the gene groups as identified in the present study.

Example 2

We report here a gene expression profile, derived from histologically normal large airway epithelial cells of current and former smokers with clinical suspicion of lung cancer that is highly sensitive and specific for the diagnosis of lung cancer. This airway signature is effective in diagnosing lung cancer at an early and potentially resectable stage. When combined with results from bronchoscopy (i.e. washings, brushings, and biopsies of the affected area), the expression profile is diagnostic of lung cancer in 95% of cases. We further show that the airway epithelial field of injury involves a number of genes that are differentially expressed in lung cancer tissue, providing potential information about pathways that may be involved in the genesis of lung cancer.

Patient Population: We obtained airway brushings from current and former smokers (n=208) undergoing fiber optic bronchoscopy as a diagnostic study for clinical suspicion of lung cancer between January 2003 and May 2005. Patients were recruited from 4 medical centers: Boston University Medical Center, Boston, Mass.; Boston Veterans Administration, West Roxbury, Mass.; Lahey Clinic, Burlington, Mass.; and Trinity College, Dublin, Ireland. Exclusion criteria included never smokers, cigar smokers and patients on a mechanical ventilator at the time of their bronchoscopy. Each subject was followed clinically, post-bronchoscopy, until a final diagnosis of lung cancer or an alternate benign diagnosis was made. Subjects were classified as having lung cancer if their bronchoscopy studies (brushing, bronchoalveolar lavage or endobronchial biopsy) or a subsequent lung biopsy (transthoracic biopsy or surgical lung biopsy) yielded tumor cells on pathology/cytology. Subjects were classified with an alternative benign diagnosis if the bronchoscopy or subsequent lung biopsy yielded a non-lung cancer diagnosis or if their radiographic abnormality resolved on follow up chest imaging. The study was approved by the Institutional Review Boards of all 4 medical centers and all participants provided written informed consent.

Airway epithelial cell collection: Following completion of the standard diagnostic bronchoscopy studies, bronchial airway epithelial cells were obtained from the “uninvolved” right mainstem bronchus with an endoscopic cytobrush (Cellebrity Endoscopic Cytobrush, Boston Scientific, Boston, Mass.). If a suspicious lesion (endobronchial or submucosal) was seen in the right mainstem bronchus, cells were then obtained from the uninvolved left mainstem bronchus. The brushes were immediately placed in TRIzol reagent (Invitrogen, Carlsbad, Calif.) after removal from the bronchoscope and kept at −80° C. until RNA isolation was performed. RNA was extracted from the brushes using TRIzol Reagent (Invitrogen) as per the manufacturer protocol, with a yield of 8-15 μg of RNA per patient. Integrity of the RNA was confirmed by denaturing gel electrophoresis. Epithelial cell content and morphology of representative bronchial brushing samples was quantified by cytocentrifugation (ThermoShandon Cytospin, Pittsburgh, Pa.) of the cell pellet and staining with a cytokeratin antibody (Signet, Dedham Mass.). These samples were reviewed by a pathologist who was blinded to the diagnosis of the patient.

Microarray data acquisition and preprocessing: 6-8 μg of total RNA was processed, labeled, and hybridized to Affymetrix HG-U133A GeneChips containing approximately 22,215 human transcripts as described previously(17). We obtained sufficient quantity of high quality RNA for microarray studies from 152 of the 208 samples. The quantity of RNA obtained improved during the course of the study so that 90% of brushings yielded sufficient high quality RNA during the latter half of the study. Log-normalized probe-level data was obtained from CEL files using the Robust Multichip Average (RMA) algorithm(18). A z-score filter was employed to filter out arrays of poor quality (see supplement for details), leaving 129 samples with a final diagnosis available for analysis.

Microarray Data Analysis: Class Prediction

To develop and test a gene expression predictor capable of distinguishing smokers with and without lung cancer, 60% of samples (n=77) representing a spectrum of clinical risk for lung cancer and approximately equal numbers of cancer and no cancer subjects were randomly assigned to a training set (see Supplement). Using the training set samples, the 22,215 probesets were filtered via ANCOVA using pack-years as the covariate; probesets with a p-value greater than 0.05 for the difference between the two groups were excluded. This training-set gene filter was employed to control for the potential confounding effect of cumulative tobacco exposure, which differed between subjects with and without cancer (see Table 1a).

Cancer NonCancer Samples 60 69 Age ** 64.1 +/− 9.0 49.8 +/− 15.2 Smoking Status 51.7% F, 48.3% C 37.7% F, 62.3% C Gender 80% M, 20% F 73.9% M, 26.1% F PackYears ** 57.4 +/− 25..6 29.4 +/− 27..3 Age Started 15.2 +/− 4.2 16.7 +/− 6.8 Smoking intensity  1.3 +/− 0.45  0.9 +/− 0.5 (PPD): Currents * Months Quit:  113 +/− 118  158 +/− 159 Formers * Two classes statistically different: p < 0.05 ** Two classes statistically different: p < 0.001

Table 1a shows demographic features and characteristics of the two patient classes being studied. Statistical differences between the two patient classes and associated p values were calculated using T-tests, Chi-square tests and Fisher's exact tests where appropriate.

Gene selection was conducted through internal cross-validation within the training set using the weighted voting algorithm(19). The internal cross-validation was repeated 50 times, and the top 40 up- and top 40 down-regulated probesets in cancer most frequently chosen during internal cross-validation runs were selected as the final gene committee of 80 features (see sections, infra, for details regarding the algorithm and the number of genes selected for the committee).

The accuracy, sensitivity, and specificity of the biomarker were assessed on the independent test set of 52 samples. This was accomplished by using the weighted vote algorithm to predict the class of each test set sample based on the gene expression of the 80 probesets and the probe set weights derived from the 77 samples in the training set. To assess the performance of our classifier, we first created 1000 predictors from the training set where we randomized the training set class labels. We evaluated the performance of these “class-randomized” classifiers for predicting the sample class of the test set samples and compared these to our classifier using ROC analysis. To assess whether the performance of our gene expression profile depends on the specific training and test sets from which it was derived and tested, we next created 500 new training and test sets with our 129 samples and derived new “sample-randomized” classifiers from each of these training sets which were then tested on the corresponding test set. To assess the specificity of our classifier genes, we next created 500 classifiers each composed of 80 randomly selected genes. We then tested the ability of these “gene-randomized” classifiers to predict the class of samples in the test set. To evaluate the robustness of our class prediction algorithm and data preprocessing, we also used these specific 80 genes to generate predictive models with an alternate class prediction algorithm (Prediction Analysis of Microarrays (PAM)(20)) and with MAS 5.0 generated expression data instead of RMA. Finally, the performance of our predictor was compared to the diagnostic yield of bronchoscopy.

Quantitative PCR Validation: Real time PCR (QRT-PCR) was used to confirm the differential expression of a select number of genes in our predictor. Primer sequences were designed with Primer Express software (Applied Biosystems, Foster City, Calif.). Forty cycles of amplification, data acquisition, and data analysis were carried out in an ABI Prism 7700 Sequence Detector (Applied Biosystems, Foster City, Calif.). All real time PCR experiments were carried out in triplicate on each sample (see sections infra).

Linking to lung cancer tissue microarray data: The 80-gene lung cancer biomarker derived from airway epithelium gene expression was evaluated for its ability to distinguish between normal and cancerous lung tissue using an Affymetrix HGU95Av2 dataset published by Bhattacharjee et al(21) that we processed using RMA. By mapping Unigene identifiers, 64 HGU95Av2 probesets were identified that measure the expression of genes that corresponded to the 80 probesets in our airway classifier. This resulted in a partial airway epithelium signature that was then used to classify tumor and normal samples from the dataset. In addition, PCA analysis of the lung tissue samples was performed using the expression of these 64 probesets.

To further assess the statistical significance of the relationship between datasets, Gene Set Enrichment Analysis(22) was performed to determine if the 64 biomarker genes are non-randomly distributed within the HGU95Av2 probesets ordered by differential expression between normal and tumor tissue. Finally, a two-tailed Fisher Exact Test was used to test if the proportion of biomarker genes among the genes differentially expressed between normal and tumor lung tissue is different from the overall proportion of differentially expressed genes (see sections, infra).

Statistical Analysis: RMA was performed in BioConductor. The upstream gene filtering by ANCOVA, and the implementation of the weighted voted algorithm and internal cross validation used to generate the data were executed through an R script we wrote for this purpose. The PAM algorithm was carried out using the ‘pamr’ library in R. All other statistical analyses including Student's T-Tests, Fisher's exact tests, ROC curves and PCA were performed using the R statistical package.

Study Population and Epithelial samples: 129 subjects that had microarrays passing the quality control filter described above were included in the class prediction analysis (see Supplemental FIG. 1). Demographic data on these subjects, including 60 smokers with primary lung cancer and 69 smokers without lung cancer is presented in Table 1, Cell type and stage information for all cancer patients is shown in Supplemental Table 1, Bronchial brushings yielded 90% epithelial cells, as determined by cytokeratin staining, with the majority being ciliated cells with normal bronchial airway morphology. No dysplastic or cancer cells were seen on any representative brushings obtained from smokers with or without cancer.

Class Prediction analysis: Comparison of demographic features for 77 subjects in the training set vs. the 52 samples in the test set is shown in Supplemental Table 2. An 80 gene class prediction committee capable of distinguishing smokers with and without cancer was built on the training set of 77 samples and tested on the independent sample set (FIG. 14). The accuracy, sensitivity and specificity of this model was 83%(43/52), 80% (16/20) and 84% (27/32) respectively. When samples predicted with a low degree of confidence (as defined by a Prediction Strength metric <0.3; see Supplement for details) were considered non-diagnostic, the overall accuracy of the model on the remaining 43 samples in the test set increased to 88% (93% sensitivity, 86% specificity). Hierarchical clustering of the 80 genes selected for the diagnostic biomarker in the test set samples is shown in FIG. 15. Principal Component Analysis of all cancer samples according to the expression of these 80 genes did not reveal grouping by cell type (FIG. 10). The accuracy of this 80-gene classifier was similar when microarray data was preprocessed in MAS 5.0 and when the PAM class prediction algorithm was used (see Supplemental Table 3).

The 80-gene predictor's accuracy, sensitivity and specificity on the 52 sample test set was significantly better than the performance of classifiers derived from randomizing the class labels of the training set (p=0.004; empiric p-value for random classifier AUC>true classifier AUC; FIG. 16). The performance of the classifier was not dependent on the particular composition of the training and test set on which it was derived and tested: 500 training and test sets (derived from the 129 samples) resulted in classifiers with similar accuracy as the classifier derived from our training set (FIG. 11). Finally, we demonstrated that the classifier is better able to distinguish the two sample classes than 500 classifiers derived by randomly selecting genes (see FIG. 12).

Real time PCR: Differential expression of select genes in our diagnostic airway profile was confirmed by real time PCR (see FIG. 13).

Linking to lung cancer tissue: Our airway biomarker was also able to correctly classify lung cancer tissue from normal lung tissue with 98% accuracy. Principal Component Analysis demonstrated separation of non-cancerous samples from cancerous samples in the Bhattacharjee dataset according to the expression of our airway signature (see FIG. 17). Furthermore, our class prediction genes were statistically over represented among genes differentially expressed between cancer vs. no cancer in the Bhattacharjee dataset by Fisher exact test (p<0.05) and Gene Enrichment Analysis (FDR<0.25, see Supplement for details).

Synergy with Bronchoscopy: Bronchoscopy was diagnostic (via endoscopic brushing, washings or biopsy of the affected region) in 32/60 (53%) of lung cancer patients and 5/69 non-cancer patients. Among non-diagnostic bronchoscopies (n=92), our class prediction model had an accuracy of 85% with 89% sensitivity and 83% specificity. Combining bronchoscopy with our gene expression signature resulted in a 95% diagnostic sensitivity (57/60) across all cancer subjects. Given the approximate 50% disease prevalence in our cohort, a negative bronchoscopy and negative gene expression signature for lung cancer resulted in a 95% negative predictive value (NPV) for disease (FIG. 18). In patients with a negative bronchoscopy, the positive predictive value of our gene expression profile for lung cancer was approximately 70% (FIG. 18).

Stage and cell type subgroup analysis: The diagnostic yield of our airway gene expression signature vs. bronchoscopy according to stage and cell type of the lung cancer samples is shown in FIG. 19.

Lung cancer is the leading cause of death from cancer in the United States, in part because of the lack of sensitive and specific diagnostic tools that are useful in early-stage disease. With approximately 90 million former and current smokers in the U.S., physicians increasingly encounter smokers with clinical suspicion for lung cancer on the basis of an abnormal radiographic imaging study and/or respiratory symptoms. Flexible bronchoscopy represents a relatively noninvasive initial diagnostic test to employ in this setting. This study was undertaken in order to develop a gene expression-based diagnostic, that when combined with flexible bronchoscopy, would provide a sensitive and specific one-step procedure for the diagnosis of lung cancer. Based on the concept that cigarette smoking creates a respiratory tract “field defect”, we examined the possibility that profiles of gene expression in relatively easily accessible large airway epithelial cells would serve as an indicator of the amount and type of cellular injury induced by smoking and might provide a diagnostic tool in smokers who were being evaluated for the possibility of lung cancer.

We have previously shown that smoking induces a number of metabolizing and antioxidant genes, induces expression of several putative oncogenes and suppresses expression of several potential tumor suppressor genes in large airway epithelial cells(17). We show here that the pattern of airway gene expression in smokers with lung cancer differs from smokers without lung cancer, and the expression profile of these genes in histologically normal bronchial epithelial cells can be used as a sensitive and specific predictor of the presence of lung cancer. We found that the expression signature was particularly useful in early stage disease where bronchoscopy was most often negative and where most problems with diagnosis occur. Furthermore, combining the airway gene expression signature with bronchoscopy results in a highly sensitive diagnostic approach capable of identifying 95% of lung cancer cases.

Given the unique challenges to developing biomarkers for disease using DNA microarrays(23), we employed a rigorous computational approach in the evaluation of our dataset. The gene expression biomarker reported in this paper was derived from a training set of samples obtained from smokers with suspicion of lung cancer and was tested on an independent set of samples obtained from four tertiary medical centers in the US and Ireland. The robust nature of this approach was confirmed by randomly assigning samples into separate training and test sets and demonstrating a similar overall accuracy (FIG. 11). In addition, the performance of our biomarker was significantly better than biomarkers obtained via randomization of class labels in the training set (FIG. 16) or via random 80 gene committees (FIG. 8). Finally, the performance of our 80-gene profile remained unchanged when microarray data was preprocessed via a different algorithm or when a second class prediction algorithm was employed.

In terms of limitations, our study was not designed to assess performance as a function of disease stage or subtype. Our gene expression predictor, however, does appear robust in early stage disease compared with bronchoscopy (see FIG. 19). Our profile was able to discriminate between cancer and no cancer across all subtypes of lung cancer (see FIG. 10). 80% of the cancers in our dataset were NSCLC and our biomarker was thus trained primarily on events associated with that cell type. However, given the high yield for bronchoscopy alone in the diagnosis of small cell lung cancer, this does not limit the sensitivity and negative predictive value of the combined bronchoscopy and gene expression signature approach. A large-scale clinical trial is needed to validate our signature across larger numbers of patients and establish its efficacy in early stage disease as well as its ability to discriminate between subtypes of lung cancer.

In addition to serving as a diagnostic biomarker, profiling airway gene expression across smokers with and without lung cancer can also provide insight into the nature of the “field of injury” reported in smokers and potential pathways implicated in lung carcinogenesis. Previous studies have demonstrated allelic loss and methylation of tumor suppressor genes in histologically normal bronchial epithelial cells from smokers with and without lung cancer(12;13;15). Whether these changes are random mutational effects or are directly related to lung cancer has been unclear. The finding that our airway gene signature was capable of distinguishing lung cancer tissue from normal lung (FIG. 4) suggests that the airway biomarker is, at least in part, reflective of changes occurring in the cancerous tissue and may provide insights into lung cancer biology.

Among the 80 genes in our diagnostic signature, a number of genes associated with the RAS oncogene pathway, including Rab 1a and FOS, are up regulated in the airway of smokers with lung cancer. Rab proteins represent a family of at least 60 different Ras-like GTPases that have crucial roles in vesicle trafficking, signal transduction, and receptor recycling, and dysregulation of RAB gene expression has been implicated in tumorigenesis(24). A recent study by Shimada et al.(25) found a high prevalence of Rab1A-overexpression in head and neck squamous cell carcinomas and also in premalignant tongue lesions, suggesting that it may be an early marker of smoking-related respiratory tract carcinogenesis.

In addition to these RAS pathway genes, the classifier contained several pro-inflammatory genes, including Interleukin-8 (IL-8) and beta-defensin 1 that were up regulated in smokers with lung cancer. IL-8, originally discovered as a chemotactic factor for leukocytes, has been shown to contribute to human cancer progression through its mitogenic and angiogenic properties(26;27). Beta defensins, antimicrobial agents expressed in lung epithelial cells, have recently found to be elevated in the serum of patients with lung cancer as compared to healthy smokers or patients with pneumonia(28). Higher levels of these mediators of chronic inflammation in response to tobacco exposure may result in increased oxidative stress and contribute to tumor promotion and progression in the lung(29;30)

A number of key antioxidant defense genes were found to be decreased in airway epithelial cells of subjects with lung cancer, including BACH2 and dual oxidase 1, along with a DNA repair enzyme, DNA repair protein 1C. BACH-2, a transcription factor, promotes cell apoptosis in response to high levels of oxidative-stress(31). We have previously found that a subset of healthy smokers respond differently to tobacco smoke, failing to induce a set of detoxification enzymes in their normal airway epithelium, and that these individuals may be predisposed to its carcinogenic effects(17). Taken together, these data suggest that a component of the airway “field defect” may reflect whether a given smoker is appropriately increasing expression of protective genes in response to the toxin. This inappropriate response may reflect a genetic susceptibility to lung cancer or alternatively, epigenetic silencing or deletion of that gene by the carcinogen.

In summary, our study has identified an airway gene expression biomarker that has the potential to directly impact the diagnostic evaluation of smokers with suspect lung cancer. These patients usually undergo fiberoptic bronchoscopy as their initial diagnostic test. Gene expression profiling can be performed on normal-appearing airway epithelial cells obtained in a simple, noninvasive fashion at the time of the bronchoscopy, prolonging the procedure by only 3-5 minutes, without adding significant risks. Our data strongly suggests that combining results from bronchoscopy with the gene expression biomarker substantially improves the diagnostic sensitivity for lung cancer (from 53% to 95%). In a setting of 50% disease prevalence, a negative bronchoscopy and negative gene expression signature for lung cancer results in a 95% negative predictive value (NPV), allowing these patients to be followed non-aggressively with repeat imaging studies. For patients with a negative bronchoscopy and positive gene expression signature, the positive predictive value is ˜70%, and these patients would likely require further invasive testing (i.e. transthoracic needle biopsy or open lung biopsy) to confirm the presumptive lung cancer diagnosis. However, this represents a substantial reduction in the numbers of patients requiring further invasive diagnostic testing compared to using bronchoscopy alone. In our study, 92/129 patients were bronchoscopy negative and would have required further diagnostic work up. However, the negative predictive gene expression profile in 56 of these 92 negative bronchoscopy subjects would leave only 36 subjects who would require further evaluation (see FIG. 18).

The cross-sectional design of our study limits interpretation of the false positive rate for our signature. Given that the field of injury may represent whether a smoker is appropriately responding to the toxin, derangements in gene expression could precede the development of lung cancer or indicate a predisposition to the disease. Long-term follow-up of the false positive cases is needed (via longitudinal study) to assess whether they represent smokers who are at higher risk for developing lung cancer in the future. If this proves to be true, our signature could serve as a screening tool for lung cancer among healthy smokers and have the potential to identify candidates for chemoprophylaxis trials.

Study Patients and Sample Collection

A. Primary sample set We recruited current and former smokers undergoing flexible bronchoscopy for clinical suspicion of lung cancer at four tertiary medical centers. All subjects were older than 21 years of age and had no contraindications to flexible bronchoscopy including hemodynamic instability, severe obstructive airway disease, unstable cardiac or pulmonary disease (i.e. unstable angina, congestive heart failure, respiratory failure) inability to protect airway or altered level of consciousness and inability to provide informed consent. Never smokers and subjects who only smoked cigars were excluded from the study. For each consented subject, we collected data regarding their age, gender, race, and a detailed smoking history including age started, age quit, and cumulative tobacco exposure. Former smokers were defined as patients who had not smoked a cigarette for at least one month prior to entering our study. All subjects were followed, post-bronchoscopy, until a final diagnosis of lung cancer or an alternative diagnosis was made (mean follow-up time=52 days). For those patients diagnosed with lung cancer, the stage and cell type of their tumor was recorded. The clinical data collected from each subject in this study can be accessed in a relational database at http://pulm.bumc.bu.edu/CancerDx/. The stage and cell type of the 60 cancer samples used to train and test the class prediction model is shown in Supplemental Table 1 below.

Stage Cell Type NSCLC staging NSCLC 48 IA 2 Squamous Cell 23 IB 9 Adenocarcinoma 11 IIA 2 Large Cell 4 IIB 0 Not classified 10 IIIA 9 Small Cell 11 IIIB 9 Unknown 1 IV 17

Supplemental Table 1 above shows cell type and staging information for 60 lung cancer patients in the 129 primary sample set used to build and test the class prediction model. Staging information limited to the 48 non-small cell samples.

The demographic features of the samples in training and test shown are shown in Supplemental Table 2 below. The Table shows patient demographics for the primary dataset (n=129) according to training and test set status. Statistical differences between the two patient classes and associated p values were calculated using T-tests, Chi-square tests and Fisher's exact tests where appropriate. PPD=packs per day, F=former smokers, C=current smokers, M=male, F=female.

Training set Test set Samples 77 52 Age 59.3 +/− 13.1 52.1 +/− 15.6 Smoking Status 41.6% F, 58.4% C 48.1% F, 51.9% C Gender* 83.1% M, 16.9% F 67.3% N, 32.7% F PackYears 45.6 +/− 31 37.7 +/− 27.8 Age Started 16.2 +/− 6.3 15.8 +/− 5.3 Smoking intensity  1.1 +/− 0.53   1 +/− 0.5 (PPD): Currents Months Quit:  128 +/− 139  139 +/− 141 Formers *Two classes statistically different: p < 0.05

While our study recruited patients whose indication for bronchoscopy included a suspicion for lung cancer, each patient's clinical pre-test probability for disease varied. In order to ensure that our class prediction model was trained on samples representing a spectrum of lung cancer risk, three independent pulmonary clinicians, blinded to the final diagnoses, evaluated each patient's clinical history (including age, smoking status, cumulative tobacco exposure, co-morbidities, symptoms/signs and radiographic findings) and assigned a pre-bronchoscopy probability for lung cancer. Each patient was classified into one of three risk groups: low (<10% probability of lung cancer), medium (10-50% probability of lung cancer) and high (>50% probability of lung cancer). The final risk assignment for each patient was decided by the majority opinion.

Prospective Sample Set:

After completion of the primary study, a second set of samples was collected from smokers undergoing flexible bronchoscopy for clinical suspicion of lung cancer at 5 medical centers (St. Elizabeth's Hospital in Boston, Mass. was added to the 4 institutions used for the primary dataset). Inclusion and exclusion criteria were identical to the primary sample set. Forty additional subjects were included in this second validation set. Thirty-five subjects had microarrays that passed our quality-control filter. Demographic data on these subjects, including 18 smokers with primary lung cancer and 17 smokers without lung cancer, is presented in Supplemental Table 3. There was no statistical difference in age or cumulative tobacco exposure between case and controls in this prospective cohort (as opposed to the primary dataset; see Table 1a).

Supplemental Table 3 below shows patient demographics for the prospective validation set (n=35) by cancer status. Statistical differences between the two patient classes and associated p values were calculated using T-tests, Chi-square tests and Fisher's exact tests where appropriate. PPD=packs per day, F=former smokers, C=current smokers, M=male, F=female.

Cancer No Cancer Samples 18 17 Age 66.1 +/− 11.4 62.2 +/− 11.1 Smoking Status 66.7 F, 33.3% C 52.9% F, 47.1% C Gender* 66.6% M, 33.3% F 70.6% M, 29.4% F PackYears 46.7 +/− 28.8   60 +/− 44.3 Age Started 16.4 +/− 7.3 14.2 +/− 3.8 Smoking intensity  1.1 +/− 0.44  1.2 +/− 0.9 (PPD): Currents Months Quit:  153 +/− 135   93 +/− 147 Formers *Two classes statistically different: p < 0.05 Airway Epithelial Cell Collection:

Bronchial airway epithelial cells were obtained from the subjects described above via flexible bronchoscopy. Following local anesthesia with 2% topical lidocaine to the oropharynx, flexible bronchoscopy was performed via the mouth or nose. Following completion of the standard diagnostic bronchoscopy studies (i.e. bronchoalveolar lavage, brushing and endo/transbronchial biopsy of the affected region), brushings were obtained via three endoscopic cytobrushes from the right mainstem bronchus. The cytobrush was rubbed over the surface of the airway several times and then retracted from the bronchoscope so that epithelial cells could be placed immediately in TRIzol solution and kept at −80° C. until RNA isolation was performed.

Given that these patients were undergoing bronchoscopy for clinical indications, the risks from our study were minimal, with less than a 5% risk of a small amount of bleeding from these additional brushings. The clinical bronchoscopy was prolonged by approximately 3-4 minutes in order to obtain the research samples. All participating subjects were recruited by IRB-approved protocols for informed consent, and participation in the study did not affect subsequent treatment. Patient samples were given identification numbers in order to protect patient privacy.

Microarray Data Acquisition and Preprocessing

Microarray data acquisition: 6-8 μg of total RNA from bronchial epithelial cells were converted into double-stranded cDNA with SuperScript II reverse transcriptase (Invitrogen) using an oligo-dT primer containing a T7 RNA polymerase promoter (Genset, Boulder, Colo.). The ENZO Bioarray RNA transcript labeling kit (Enzo Life Sciences, Inc, Farmingdale, N.Y.) was used for in vitro transcription of the purified double stranded cDNA. The biotin-labeled cRNA was then purified using the RNeasy kit (Qiagen) and fragmented into fragments of approximately 200 base pairs by alkaline treatment. Each cRNA sample was then hybridized overnight onto the Affymetrix HG-U133A array followed by a washing and staining protocol. Confocal laser scanning (Agilent) was then performed to detect the streptavidin-labeled fluor.

Preprocessing of array data via RMA: The Robust Multichip Average (RMA) algorithm was used for background adjustment, normalization, and probe-level summarization of the microarray samples in this study (Irizarry R A, et al., Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res 2003; 31(4):e15.). RMA expression measures were computed using the R statistical package and the justRMA function in the Affymetrix Bioconductor package. A total of 296 CEL files from airway epithelial samples included in this study as well as those previously processed in our lab were analyzed using RMA. RMA was chosen for probe-level analysis instead of Micro array Suite 5.0 because it maximized the correlation coefficients observed between 7 pairs of technical replicates (Supplemental Table 4).

SUPPLEMENTAL TABLE 4 Pearson Correlation Coefficients (22,215 probe-sets) Affy log2Affy RMA Average 0.972 0.903 0.985 SD 0.017 0.029 0.009 Median 0.978 0.912 0.987

Supplemental Table 4 shows the Average Pearson Correlations between 7 pairs of replicate samples where probe-set gene expression values were determined using Microarray Suite 5.0 (Affy), logged data from Microarray Suite 5.0 (log 2 Affy), and RMA. RMA maximizes the correlation between replicate samples.

Sample filter: To filter out arrays of poor quality, each probeset on the array was z-score normalized to have a mean of zero and a standard deviation of 1 across all 152 samples. These normalized gene-expression values were averaged across all probe-sets for each sample. The assumption explicit in this analysis is that poor-quality samples will have probeset intensities that consistently trend higher or lower across all samples and thus have an average z-score that differs from zero. This average z-score metric correlates with Affymetrix MAS 5.0 quality metrics such as percent present (FIG. 7) and GAPDH 3′/5′ ratio. Microarrays that had an average z-score with a value greater than 0.129 (˜15% of the 152 samples) were filtered out. The resulting sample set consisted of 60 smokers with cancer and 69 smokers without cancer.

Prospective validation test set: CEL files for the additional 40 samples were added to the collection of airway epithelial CEL files described above, and the entire set was analyzed using RMA to derive expression values for the new samples. Microarrays that had an average z-score with a value greater than 0.129 (5 of the 40 samples) were filtered out. Class prediction of the 35 remaining prospective samples was conducted using the vote weights for the 80-predictive probesets derived from the training set of 77 samples using expression values computed in the section above.

Microarray Data Analysis

Class Prediction Algorithm: The 129-sample set (60 cancer samples, 69 no cancer samples) was used to develop a class-prediction algorithm capable of distinguishing between the two classes. One potentially confounding difference between the two groups is a difference in cumulative tobacco-smoke exposure as measured by pack-years. To insure that the genes chosen for their ability to distinguish patients with and without cancer in the training set were not simply distinguishing this difference in tobacco smoke exposure, the pack-years each patient smoked was included as a covariate in the training set ANCOVA gene filter.

In addition, there are differences in the pre-bronchoscopy clinical risk for lung cancer among the 129 patients. Three physicians reviewed each patient's clinical data (including demographics, smoking histories, and radiographic findings) and divided the patients into three groups: high, medium, and low pre-bronchoscopy risk for lung cancer (as described above). In order to control for differences in pre-bronchoscopy risk for lung cancer between the patients with and without a final diagnosis of lung cancer, the training set was constructed with roughly equal numbers of cancer and no cancer samples from a spectrum of lung cancer risk.

The weighted voting algorithm (Golub T R, et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999; 286(5439):531-537) was implemented as the class prediction method, with several modifications to the gene-selection methodology. Genes that varied between smokers with and without cancer in the training set samples after adjusting for tobacco-smoke exposure (p<0.05) were identified using an ANCOVA with pack-years as the covariate. Further gene selection was performed using the signal to noise metric and internal cross-validation where the 40 most consistently up- and the 40 most consistently down-regulated probesets were identified. The internal cross validation involved leaving 30% of the training samples out of each round of cross-validation, and selecting genes based on the remaining 70% of the samples. The final gene committee consisted of eighty probesets that were identified as being most frequently up-regulated or down-regulated across 50 rounds of internal cross-validation. The parameters of this gene-selection algorithm were chosen to maximize the average accuracy, sensitivity and specificity obtained from fifty runs. This algorithm was implemented in R and yields results that are comparable to the original implementation of the weighted-voted algorithm in GenePattern when a specific training, test, and gene set are given as input.

After determination of the optimal gene-selection parameters, the algorithm was run using a training set of 77 samples to arrive at a final set of genes capable of distinguishing between smokers with and without lung cancer. The accuracy, sensitivity and specificity of this classifier were tested against 52 samples that were not included in the training set. The performance of this classifier in predicting the class of each test-set sample was assessed by comparing it to runs of the algorithm where either: 1) different training/tests sets were used; 2) the cancer status of the training set of 77 samples were randomized; or 3) the genes in the classifier were randomly chosen (see randomization section below for details).

Randomization: The accuracy, sensitivity, specificity, and area under the ROC curve (using the signed prediction strength as a continuous cancer predictor) for the 80-probeset predictor (above) were compared to 1000 runs of the algorithm using three different types of randomization. First, the class labels of the training set of 77 samples were permuted and the algorithm, including gene selection, was re-run 1000 times (referred to in Supplemental Table 5 as Random 1).

Supplemental Table 5 below shows results of a comparison between the actual classifier and random runs (explained above). Accur=Accuracy, Sens=Sensitivity, Spec=Specificity, AUC=area under the curve, and sd=standard deviation. All p-value are empirically derived.

SUPPLEMENTAL TABLE 5 Accur sd(Accur) p-value Sens sd(Sens) p-value Spec sd(Spec) p-value AUC sd(AUC) p-value Actual Classifier 0.827 0.8 0.844 0.897 Random 1 0.491 0.171 0.018 0.487 0.219 0.114 0.493 0.185 0.015 0.487 0.223 0.004 Random 2 0.495 0.252 0.078 0.496 0.249 0.173 0.495 0.263 0.073 0.495 0.309 0.008 Random 3 0.495 0.193 0.021 0.491 0.268 0.217 0.498 0.17 0.006 0.492 0.264 0.007

The second randomization used the 80 genes in the original predictor but permuted the class labels of the training set samples over 1000 runs to randomize the gene weights used in the classification step of the algorithm (referred to in Supplemental Table 5 as Random 2).

In both of these randomization methods, the class labels were permuted such that half of the training set samples was labeled correctly. The third randomization method involved randomly selecting 80 probesets for each of 1000 random classifiers (referred to in Supplemental Table 5 as Random 3).

The p-value for each metric and randomization method shown indicate the percentage of 1000 runs using that randomization method that exceeded or was equal to the performance of the actual classifier.

In addition to the above analyses, the actual classifier was compared to 1000 runs of the algorithm where different training/test sets were chosen but the correct sample labels were retained. Empirically derived p-values were also computed to compare the actual classifier to the 1000 runs of the algorithm (see Supplemental Table 6). These data indicate that the actual classifier was derived using a representative training and test set.

SUPPLEMENTAL TABLE 6 Accur sd(Accur) p-value Sens sd(Sens) p-value Spec sd(Spec) p-value AUC sd(AUC) p-value Actual Classifier 0.827 0.8 0.844 0.897 1000 Runs 0.784 0.054 0.283 0.719 0.104 0.245 0.83 0.06 0.407 0.836 0.053 0.108

Supplemental Table 6 above shows a comparison of actual classifier to 1000 runs of the algorithm with different training/test sets.

Finally, these 1000 runs of the algorithm were also compared to 1000 runs were the class labels of different training sets were randomized in the same way as described above. Empirically derived p-values were computed to compare 1000 runs to 1000 random runs (Supplemental Table 7).

SUPPLEMENTAL TABLE 7 Accur sd(Accur) p-value Sens sd(Sens) p-value Spec sd(Spec) p-value AUC sd(AUC) p-value 1000 Runs 0.784 0.054 0.719 0.104 0.83 0.06 0.836 0.053 1000 Random Runs 0.504 0.126 0.002 0.501 0.154 0.025 0.506 0.154 0.003 0.507 0.157 0.001

Supplemental Table 7 above shows comparison of runs of the algorithm using different training/test sets to runs where the class labels of the training sets were randomized (1000 runs were conducted).

The distribution of the prediction accuracies summarized in Supplemental Tables 6 and 7 is shown in FIG. 8.

Characteristics of the 1000 additional runs of the algorithm: The number of times a sample in the test set was classified correctly and its average prediction strength was computed across the 1000 runs of the algorithm. The average prediction strength when a sample was classified correctly was 0.54 for cancers and 0.61 for no cancers, and the average prediction strength when a sample was misclassified was 0.31 for cancer and 0.37 for no cancers. The slightly higher prediction strength for smokers without cancer is reflective of the fact that predictors have a slightly higher specificity on average. Supplemental FIG. 3 shows that samples that are consistently classified correctly or classified incorrectly are classified with higher confidence higher average prediction strength). Interestingly, 64% of the samples that are consistently classified incorrectly (incorrect greater than 95% of the time, n=22 samples) are samples from smokers that do not currently have a final diagnosis of cancer. This significantly higher false-positive rate might potentially reflect the ability of the biomarker to predict future cancer occurrence or might indicate that a subset of smokers with a cancer-predisposing gene-expression phenotype are protected from developing cancer through some unknown mechanism.

In order to further assess the stability of the biomarker gene committee, the number of times the 80-predictive probesets used in the biomarker were selected in each of the 1000 runs (Supplemental Table 6) was examined. (See FIG. 10A) The majority of the 80-biomarker probesets were chosen frequently over the 1000 runs (37 probesets were present in over 800 runs, and 58 of the probesets were present in over half of the runs). For purposes of comparison, when the cancer status of the training set samples are randomized over 1000 runs (Supplemental Table 7), the most frequently selected probeset is chosen 66 times, and the average is 7.3 times. (See FIG. 10B).

Comparison of RMA vs. MAS 5.0 and weighted voting vs. PAM: To evaluate the robustness of our ability to use airway gene expression to classify smokers with and without lung cancer, we examined the effect of different class-prediction and data preprocessing algorithms. We tested the 80-probesets in our classifier to generate predictive models using the Prediction Analysis of Microarrays (PAM) algorithm (Tibshirani R, et al., Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci USA 2002; 99(10):6567-6572), and we also tested the ability of the WV algorithm to use probeset level data that had been derived using the MAS 5.0 algorithm instead of RMA. The accuracy of the classifier was similar when microarray data was preprocessed in MAS 5.0 and when the PAM class prediction algorithm was used (see Supplemental Table 8).

SUPPLEMENTAL TABLE 8 Accuracy Sensitivity Specificity WV-RMA data 82.69% 80% 84.38% PAM-RMA data 86.54% 90% 84.38% WV-MAS5 data 82.69% 80% 84.38% PAM-MAS5 data 86.54% 95% 81.25%

Supplemental Table 8 shows a comparison of accuracy, sensitivity and specificity for our 80 probeset classifier on the 52 sample test set using alternative microarray data preprocessing algorithms and class prediction algorithms.

Prediction strength: The Weighted voting algorithm predicts a sample's class by summing the votes each gene on the class prediction committee gives to one class versus the other. The level of confidence with which a prediction is made is captured by the Prediction Strength (PS) and is calculated as follows:

${PS} = \frac{V_{winning} - V_{losing}}{V_{winning} + V_{losing}}$

V_(winning) refers to the total gene committee votes for the winning class and V_(losing) refers to the total gene committee votes for the losing class. Since V_(winning) is always greater than V_(losing), PS confidence varies from 0 (arbitrary) to 1 (complete confidence) for any given sample.

In our test set, the average PS for our gene profile's correct predictions (43/52 test samples) is 0.73 (+/− 0.27), while the average PS for the incorrect predictions (9/52 test samples) is much lower: 0.49 (+/− 0.33; p<z; Student T-Test). This result shows that, on average, the Weighted Voting algorithm is more confident when it is making a correct prediction than when it is making an incorrect prediction. This result holds across 1000 different training/test set pairs (FIG. 11):

Cancer cell type: To determine if the tumor cell subtype affects the expression of genes that distinguish airway epithelium from smokers with and without lung cancer, Principal Component Analysis (PCA) was performed on the gene-expression measurements for the 80 probesets in our predictor and all of the airway epithelium samples from patients with lung cancer (FIG. 12). Gene expression measurements were Z(0,1) normalized prior to PCA. There is no apparent separation of the samples with regard to cancer subtype.

Link to Lung Cancer Tissue Microarray Dataset

Preprocessing of Bhattacharjee data: The 254 CEL files from HgU95Av2 arrays used by Bhattacharjee et al. (Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc Natl Acad Sci USA 2001; 98(24):13790-13795) were downloaded from the MIT Broad Institute's database available through internet (broad.mit.edu/mpr/lung). RMA-derived expression measurements were computed using these CEL files as described above. Technical replicates were filtered by choosing one at random to represent each patient. In addition, arrays from carcinoid samples and patients who were indicated to have never smoked were excluded, leaving 151 samples. The z-score quality filter described above was applied to this data set resulting in 128 samples for further analysis (88 adenocarcinomas, 3 small cell, 20 squamous, and 17 normal lung samples).

Probesets were mapped between the HGU133A array and HGU95Av2 array using Chip Comparer at the Duke University's database available through the world wide web at tenero.duhs.duke.edu/genearray/perl/chip/chipcomparer.pl. 64 probesets on the HGU95Av2 array mapped to the 80-predictive probesets. The 64 probesets on the HGU95Av2 correspond to 48 out of the 80 predictive probesets (32/80 predictive probesets have no clear corresponding probe on the HGU95Av2 array).

Analyses of Bhattacharjee dataset: In order to explore the expression of genes that we identified as distinguishing large airway epithelial cells from smokers with and without lung cancer in lung tumors profiled by Bhattacharjee, two different analyses were performed. Principal component analysis was used to organize the 128 Bhattacharjee samples according to the expression of the 64 mapped probesets. Principal component analysis was conducted in R using the package prcomp on the z-score normalized 128 samples by 64 probeset matrix. The normal and malignant samples in the Bhattacharjee dataset appear to separate along principal component 1 (see FIG. 17). To assess the significance of this result, the principal component analysis was repeated using the 128 samples and 1000 randomly chosen sets of 64 probesets. The mean difference between normal and malignant samples was calculated based on the projected values for principal component 1 for the actual 64 probesets and for each of the 1000 random sets of 64 probesets. The mean difference between normal and malignant from the 1000 random gene sets was used to generate a null distribution. The observed difference between the normal and malignant samples using the biomarker probesets was greater than the difference observed using randomly selected genes (p=0.026 for mean difference and p=0.034 for median difference).

The second analysis involved using the weighted voted algorithm to predict the class of 108 samples in the Bhattacharjee dataset using the 64 probesets and a training set of 10 randomly chosen normal tissues and 10 randomly chosen tumor tissues. The samples were classified with 89.8% accuracy, 89.1% sensitivity, and 100% specificity (see Supplemental Table 9 below, Single Run). To examine the significance of these results, the weighted voted algorithm was re-run using two types of data randomization. First, the class labels of the training set of 20 samples were permuted and the algorithm, including gene selection, was re-run 1000 times (referred to in Supplemental Table 9 as Random 1). The second randomization involved permuting the class labels of the training set of 20 samples and re-running the algorithm 1000 times keeping the list of 64-probsets constant (referred to in Supplemental Table 9 as Random 2). In the above two types of randomization, the class labels were permuted such that half the samples were correctly labeled. The p-value for each metric and randomization method shown indicate the percentage of 1000 runs using that randomization method that exceeded or were equal to the performance of the actual classifier. Genes that distinguish between large airway epithelial cells from smokers with and without cancer are significantly better able to distinguish lung cancer tissue from normal lung tissue than any random run where the class labels of the training set are randomized.

SUPPLEMENTAL TABLE 9 Accur sd(Accur) p-value Sens sd(Sens) p-value Spec sd(Spec) p-value AUC sd(AUC) p-value Single Run 0.898 0.891 1 0.984 Random 1 0.486 0.218 0.007 0.486 0.217 0.008 0.484 0.352 0.131 0.481 0.324 0.005 Random 2 0.498 0.206 0.009 0.499 0.201 0.011 0.494 0.344 0.114 0.494 0.324 0.014

Supplemental Table 9 above shows results of a comparison between the predictions of the Bhattacharjee samples using the 64 probesets that map to a subset of the 80-predictive probesets and random runs (explained above). Accur=Accuracy, Sens=Sensitivity, Spec=Specificity, AUC=area under the curve, and sd=standard deviation.

Real Time PCR: Quantitative RT-PCR analysis was used to confirm the differential expression of a seven genes from our classifier. Primer sequences for the candidate genes and a housekeeping gene, the 18S ribosomal subunit, were designed with PRIMER EXPRESS® software (Applied Biosystems) (see Supplemental Table 10).

SUPPLEMENTAL TABLE 10 Candidate and housekeeping gene primers for real time PCR aasay Gene Ensembl Symbol Affy ID ID Name Forward Primer Reverse Primer BACH2 215907_at ENSG000001 BTB and CNC TGGCAAAACCGCATCTCT ACCACCATGCCCAGCTAA 12182 homology 1,  AC(SEQ ID No. 1) (SEQ ID No. 2) basic leucine zipper trans- cription factor 2 DCLRE1 219678_x ENSG000001 DNA cross- GCACTTTGAGGTGGGCAA CCAGGCTGGTGTCGAACTC C _at 52457 link repair  T (SEQ ID No. 3) (SEQ ID No. 4) 1C DUOX1 215800_at ENSG000001 dual  GAGAGAAAGCAAAGGAG CATGTGAGTCTGAAATTACAGCATT 37857 oxidase 1 TGAACTT (SEQ ID No. 6) (SEQ ID No. 5) FOS 209189_at ENSG000001 v-fos FBJ  AGATGTAGCAAAACGCAT CTCTGAAGTGTCACTGGGAACA 70345 murine GGA (SEQ ID No. 8) osteosarcoma (SEQ ID No. 7) viral oncogene homolog IL8 211506_s ENSG000001 interleukin  GCTAAAGAACTTAGATGT GGTGGAAAGGTTTGGAGTATGTC _at 69429 8 CAGTGCAT (SEQ ID No. 10) (SEQ ID No. 9) RABIA 207791_s ENSG000001 RAB1A,  GGAGCCCATGGCATCATA TTGAAGGACTCCTGATCTGTCA _at 38069 member RAS  (SEQ ID No. 11) (SEQ ID No. 12) oncogene family 18S TTTCGGAACTGAGGCCAT TTTCGCTCTGGTCCGTCTT G (SEQ ID No. 16) (SEQ ID No. 15) GAPDH TGCACCACCAACTGCTTA GGCATGGACTGTGGTCATGAG GC (SEQ ID No. 18) (SEQ ID No. 17) HPRT1 TGACACTGGCAAAACAAT GGTCCTTTTCACCAGCAAGCT GCA (SEQ ID No. 20) (SEQ ID No. 19) SDHA TGGGAACAAGAGGGCATC CCACCACTGCATCAAATTCATG TG (SEQ ID No. 22) (SEQ ID No. 21) TBP TGCACAGGAGCCAAGAGT CACATCACAGCTCCCCACCA GAA (SEQ ID No. 24) (SEQ ID No. 23) YWHAZ ACTTTTGGTACATTGTGG CCGCCAGGACAAACCAGTAT CTTCAA (SEQ ID No. 26) (SEQ ID No. 25)

Primer sequences for five other housekeeping genes (HPRT1, SDHA, YWHAZ, GAPDH, and TBP) were adopted from Vandesompele et al. (Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol 2002; 3(7)). RNA samples (1 μg of the RNA used in the microarray experiment) were treated with DNAfree (Ambion, Austin, Tex.), according to the manufacturer's protocol, to remove contaminating genomic DNA. Total RNA was reverse-transcribed using random hexamers (Applied Biosystems) and SuperScript II reverse transcriptase (Invitrogen). The resulting first-strand cDNA was diluted with nuclease-free water (Ambion) to 5 ng/μl. PCR amplification mixtures (25 μl) contained 10 ng template cDNA, 12.5 μl of 2× SYBR Green PCR master mix (Applied Biosystems) and 300 nM forward and reverse primers. Forty cycles of amplification and data acquisition were carried out in an Applied Biosystems 7500 Real Time PCR System. Threshold determinations were automatically performed by Sequence Detection Software (version 1.2.3) (Applied Biosystems) for each reaction. All real-time PCR experiments were carried out in triplicate on each sample (6 samples total; 3 smokers with lung cancer and 3 smokers without lung cancer).

Data analysis was performed using the geNorm tool (Id.). Three genes (YWHAZ, GAPDH, and TBP) were determined to be the most stable housekeeping genes and were used to normalize all samples. Data from the QRT-PCR for 7 genes along with the microarray results for these genes is shown in FIG. 13.

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We claim:
 1. A method of processing a sample of airway epithelial cells from a subject who is asymptomatic for lung cancer, comprising: (a) receiving a sample of airway epithelial cells from the subject who is asymptomatic for lung cancer; and (b) measuring, by reverse-transcription polymerase chain reaction (RT-PCR) analysis, the expression level of gene transcripts in the sample, wherein the gene transcripts measured comprise IL-8, FOS, and BACH2.
 2. The method of claim 1 wherein the gene transcripts measured further comprise DEFB1.
 3. The method of claim 2 wherein the gene transcripts measured further comprise CCDC81.
 4. The method of claim 1 further comprising measuring a gene transcript selected from Table
 1. 5. The method of claim 1 further comprising measuring a gene transcript selected from Table
 3. 6. The method of claim 1 further comprising measuring a gene transcript selected from Table
 4. 7. The method of claim 1 further comprising measuring a gene transcript selected from Table
 5. 8. The method of claim 1 further comprising measuring a gene transcript selected from Table
 6. 9. The method of claim 1 further comprising measuring a gene transcript selected from Table
 7. 10. The method of claim 1 wherein transcripts of at least 40 genes are measured.
 11. The method of claim 1 wherein transcripts of at least 60 genes are measured.
 12. The method of claim 1 wherein transcripts of at least 70 genes are measured.
 13. The method of claim 1 wherein the airway epithelial cells are not from the bronchus.
 14. The method of claim 1 wherein transcripts of no more than 180 genes are measured. 